Rapid drug sensitivity detection method and device for biological sample containing low-abundance bacteria

Abstract

The invention provides a rapid drug sensitivity detection method and device for a biological sample containing low-abundance bacteria, and relates to the technical field of scattering imaging. Through a single-bacterium dynamic scattering imaging technology, direct and real-time detection of bacterium growth is realized, traditional bacterium culture, separation and enrichment steps are not needed, the detection time is remarkably shortened from traditional 2-5 days to several hours, and guidance is provided for clinical rapid medication decision-making; a single-bacterium dynamic scattering imaging technology is adopted, so that single bacteria can be accurately identified and counted, the growth condition of the bacteria is monitored in real time, and the detection sensitivity and accuracy are improved; the effective growth rate is calculated in combination with a growth inhibition model, so that the growth inhibition state and drug resistance of the bacteria can be more accurately judged.

Description

Technical Field

[0001] The present application relates to the field of scattering imaging technology, and in particular to a method and device for rapid drug sensitivity detection of biological samples containing low-abundance bacteria. Background Art

[0002] At present, the main methods of bacterial drug sensitivity testing include genotypic and phenotypic. The former detects bacterial resistance genes, which is prone to false positive and false negative results if there is insufficient prior knowledge or gene expression; the latter measures phenotypic characteristics more directly and is the gold standard drug sensitivity method, but it still requires bacterial culture, separation and enrichment, and these pre-processing steps are very time-consuming.

[0003] The use of optical imaging methods for rapid bacterial drug sensitivity testing has the advantages of visualization and real-time detection. However, although traditional optical microscopes can image bacteria, they need to be fixed on the surface. In addition, the field of view of high-resolution optical microscopes is very small, and bacteria need to be enriched in low-concentration samples, resulting in low efficiency of bacterial drug sensitivity testing. Summary of the invention

[0004] The present application provides a method and device for rapid drug sensitivity detection of biological samples containing low-abundance bacteria, which can realize the rapid identification of low-abundance bacterial biological samples and drug sensitivity detection of antibiotic sensitivity, thereby shortening the detection time, improving the pertinence and effectiveness of antibiotic use, and providing strong support for the early treatment of bacterial infections.

[0005] According to a first aspect of the present application, a method for detecting the bacterial growth state of a biological sample containing low-abundance bacteria is provided, comprising the following steps: Obtain a biological sample containing low-abundance bacteria, where the low-abundance range is greater than or equal to 1 CFU / mL and less than or equal to 10 5 CFU / mL; The biological samples are divided into two groups and added into containers containing culture medium. The target antibiotic is added into the first group, which is used as the test group, and the antibiotic is not added into the second group, which is used as the control group. At each preset time, the image data in all the first group of cuvettes are obtained by single bacteria scattering imaging to count the bacteria; wherein the first group of cuvettes includes a first cuvette and a second cuvette, the first cuvette contains a test group, and the second cuvette contains a control group; When the predetermined recording time is reached, the ratio of the effective growth rate of the first cuvette to the second cuvette is calculated, and the growth inhibition state of the bacteria is determined in combination with the inhibition threshold.

[0006] According to a second aspect of the present application, a method for measuring the minimum inhibitory concentration of a target antibiotic in a biological sample containing low-abundance bacteria is provided, comprising the following steps: Obtain a biological sample containing low-abundance bacteria, where the low-abundance range is greater than or equal to 1 CFU / mL and less than or equal to 10 5 CFU / mL; Dividing the biological samples into two groups and adding them into containers containing culture medium respectively, wherein the first group comprises a plurality of the containers, and adding different concentrations of target antibiotics into different containers respectively, as the test group, and the second group does not add antibiotics, as the control group; At preset time intervals, single bacteria scattering imaging is used to obtain image data in all second group of cuvettes to count bacteria, wherein the second group of cuvettes includes a third cuvette and a fourth cuvette, the third cuvette contains a test group, and the fourth cuvette contains a control group; When the predetermined recording time is reached, the ratio of the effective growth rate of the third cuvette to that of the fourth cuvette is calculated, and the growth inhibition state of the bacteria is determined in combination with the inhibition threshold; From the target antibiotic concentrations corresponding to the test group in which bacteria are in an inhibited state, the minimum value is selected as the minimum inhibitory concentration of the target antibiotic.

[0007] According to the third aspect of the present application, a method for rapid drug sensitivity detection of a biological sample containing low-abundance bacteria is provided, comprising the following steps: Obtain a biological sample containing low-abundance bacteria, where the low-abundance range is greater than or equal to 1 CFU / mL and less than or equal to 10 5 CFU / mL; The biological samples are divided into three groups and added into containers containing culture medium respectively, wherein the target antibiotic is added into the first group until the final concentration of the antibiotic is the sensitive breakpoint concentration, which is used as a sensitive test group, the target antibiotic is added into the second group until the final concentration of the antibiotic is the intermediate breakpoint concentration, which is used as an intermediate test group, and no antibiotic is added into the third group, which is used as a control group; At preset time intervals, single bacteria scattering imaging is used to obtain image data in all third group of cuvettes to count bacteria, wherein the third group of cuvettes includes a fifth cuvette and a sixth cuvette, the fifth cuvette includes a sensitive test group and an intermediate test group, and the sixth cuvette includes a control group; When the predetermined recording time is reached, calculating the ratio of the effective growth rate of the fifth cuvette to that of the sixth cuvette, and determining the growth inhibition state of the bacteria in combination with the inhibition threshold; Based on the bacterial growth conditions of the sensitive test group and the intermediate test group, the drug resistance of the bacteria to the target antibiotic is determined.

[0008] In one embodiment, if the bacterial concentration of the biological sample is in the range of 1-10 3CFU / mL, the biological sample is pre-cultured before being added to the container containing the culture medium; if the bacterial concentration range of the biological sample is greater than 10 5 CFU / mL, the biological sample is diluted before adding it to the container containing the culture medium.

[0009] In one embodiment, the step of obtaining image data in all third groups of cuvettes by single bacteria scattering imaging at preset time intervals to count bacteria includes: At preset time intervals, single bacteria scattering imaging is performed on all cuvettes of the third group, and an original single bacteria dynamic light scattering image sequence is recorded. Adjacent frame images are differentiated to obtain a differential image, wherein each pixel value in the differential image represents an intensity change of a pixel at a corresponding position in two adjacent frames of the original single bacteria dynamic light scattering image; if the intensity in the differential image exceeds an intensity threshold, then the related images of the differential image are removed from the original single bacteria dynamic light scattering image sequence to obtain a first original image sequence; A background image is constructed by calculating the time domain local minimum value of each pixel in the first original image sequence, and the background image is subtracted frame by frame from the first original image sequence to obtain a second original image sequence; The spatial local background of each pixel in the second original image sequence is calculated by averaging a large area within a preset radius around the pixel, and the spatial local background is subtracted from the second original image sequence to obtain a third original image sequence, wherein the radius is set to be not less than the radius of the largest target detection object; Using image processing algorithms and filters to identify individual bacteria of various shapes in the third original image sequence, to obtain a single bacteria scattered light spot image sequence; After filtering the single bacteria scattered light spot image sequence to remove abnormal points, each single bacteria scattered light spot image in the single bacteria scattered light spot image sequence is counted and the sum of the numbers is obtained, and the sum of the numbers is divided by the number of single bacteria scattered light spot image sequences to obtain the number of single bacteria.

[0010] In one embodiment, the biological sample containing low-abundance bacteria includes a nasopharyngeal swab sample, an oral swab sample, a sputum sample, a bronchoalveolar lavage fluid sample, a pleural effusion sample, an ascites sample, a cerebrospinal fluid sample, a serum sample, a plasma sample, a blood sample, a urine sample, a reproductive tract secretion sample, and a stool sample.

[0011] In one embodiment, the effective growth rate is calculated based on a growth inhibition model, and the growth inhibition model is: ; In the formula, is the change in bacterial population over time; is the bacterial growth rate constant in the absence of antibiotics; is the maximum killing rate constant; is the growth delay correction factor; is the kill delay correction factor; is the impurity term correction parameter; Based on the growth inhibition model, the number of bacterial cells at time t is calculated as: ; The effective growth rate is: .

[0012] In one embodiment, when the predetermined recording time is reached, the step of calculating the ratio of the effective growth rate of the fifth cuvette to that of the sixth cuvette, and determining the growth inhibition state of the bacteria in combination with the inhibition threshold, comprises: Calculating the ratio of the effective growth rate of bacteria in the fifth cuvette to that in the sixth cuvette; comparing the ratio to an inhibition threshold; If the ratio is lower than the inhibition threshold, the target antibiotic at this concentration has an inhibitory effect on bacterial growth, and the bacteria in the fifth cuvette are determined to be in an inhibition state; Otherwise, it is determined that the bacteria in the fifth cuvette are in a growth state.

[0013] In one embodiment, the step of determining the drug resistance of bacteria to the target antibiotic based on the bacterial growth conditions of the sensitive test group and the intermediate test group includes: Determining the bacterial growth conditions of the sensitive test group and the intermediate test group; If the bacteria in the sensitive test group and the intermediate test group do not grow, the bacteria are determined to be sensitive strains of the target antibiotic; If the bacteria in the sensitive test group grow and the bacteria in the intermediate test group do not grow, the bacteria are determined to be intermediate strains of the target antibiotic; If the bacteria in both the sensitive test group and the intermediate test group grow, it is determined that the bacteria are resistant strains of the target antibiotic.

[0014] According to a fourth aspect of the present application, a rapid drug sensitivity detection device for a biological sample containing low-abundance bacteria is provided, comprising: The acquisition module is used to acquire biological samples containing low-abundance bacteria, where the low-abundance range is greater than or equal to 1 CFU / mL and less than or equal to 10 5 CFU / mL; A grouping module, for dividing the biological sample into three groups, adding them into containers containing culture medium respectively, adding the target antibiotic into the first group until the final concentration of the antibiotic is the sensitive breakpoint concentration, serving as a sensitive test group, adding the target antibiotic into the second group until the final concentration of the antibiotic is the intermediate breakpoint concentration, serving as an intermediate test group, and not adding the antibiotic into the third group, serving as a control group; a counting module, configured to obtain image data in all third group of cuvettes by single bacteria scattering imaging at preset time intervals, and count bacteria, wherein the third group of cuvettes comprises a fifth cuvette and a sixth cuvette, the fifth cuvette comprises a sensitive test group and an intermediate test group, and the sixth cuvette comprises a control group; A calculation module, used for calculating the ratio of the effective growth rate of the fifth cuvette to that of the sixth cuvette when a predetermined recording time is reached, and determining the growth inhibition state of the bacteria in combination with an inhibition threshold; The determination module is used to determine the drug resistance of bacteria to the target antibiotic based on the bacterial growth conditions of the sensitive test group and the intermediate test group.

[0015] In one possible implementation, the counting module includes: A differential calculation unit is used to perform single bacteria scattering imaging on all the third group of cuvettes at preset time intervals, record multiple original single bacteria dynamic light scattering image sequences, and perform differential calculation on adjacent frame images to obtain differential images, wherein each pixel value in the differential image represents the intensity change of the pixel at the corresponding position in the original single bacteria dynamic light scattering images of two adjacent frames; if the intensity in the differential image exceeds an intensity threshold, then remove the related images of the differential image from the original single bacteria dynamic light scattering image sequence to obtain a first original image sequence; A time domain local minimum value calculation unit, configured to construct a background image by calculating the time domain local minimum value of each pixel in the first original image sequence, and to subtract the background image from the first original image sequence frame by frame to obtain a second original image sequence; a spatial local background calculation unit, configured to calculate the spatial local background of each pixel in the second original image sequence by averaging a large area within a preset radius around the pixel, and subtract the spatial local background from the second original image sequence to obtain a third original image sequence, wherein the radius is set to be not less than the radius of the largest target detection object; an identification unit, used for identifying individual bacteria of various shapes in the third original image sequence by using an image processing algorithm and a filter, and obtaining a single bacteria scattered light spot image sequence; A filtering and counting unit is used to filter the single bacteria scattered light spot image sequence to remove abnormal points, count each single bacteria scattered light spot image in the single bacteria scattered light spot image sequence and obtain the sum of the numbers, and divide the sum of the numbers by the number of images in the single bacteria scattered light spot image sequence to obtain the number of single bacteria.

[0016] In one embodiment, the biological sample containing low-abundance bacteria includes a nasopharyngeal swab sample, an oral swab sample, a sputum sample, a bronchoalveolar lavage fluid sample, a pleural effusion sample, an ascites sample, a cerebrospinal fluid sample, a serum sample, a plasma sample, a blood sample, a urine sample, a reproductive tract secretion sample, and a stool sample.

[0017] In one embodiment, the effective growth rate is calculated based on a growth inhibition model, and the growth inhibition model is: ; In the formula, is the change in bacterial population over time; is the bacterial growth rate constant in the absence of antibiotics; is the maximum killing rate constant; is the growth delay correction factor; is the kill delay correction factor; is the impurity term correction parameter; Based on the growth inhibition model, the number of bacterial cells at time t is calculated as: ; The effective growth rate is: ; The step of calculating the ratio of the effective growth rate of the fifth cuvette to that of the sixth cuvette when the predetermined recording time is reached, and determining the growth inhibition state of the bacteria in combination with the inhibition threshold, comprises: Calculating the ratio of the effective growth rate of bacteria in the fifth cuvette to that in the sixth cuvette; comparing the ratio to an inhibition threshold; If the ratio is lower than the inhibition threshold, the target antibiotic at this concentration has an inhibitory effect on bacterial growth, and the bacteria in the fifth cuvette are determined to be in an inhibition state; Otherwise, determining that the bacteria in the fifth cuvette are in a growth state; The step of determining the drug resistance of bacteria to the target antibiotic based on the bacterial growth conditions of the sensitive test group and the intermediate test group comprises: Determining the bacterial growth conditions of the sensitive test group and the intermediate test group; If the bacteria in the sensitive test group and the intermediate test group do not grow, the bacteria are determined to be sensitive strains of the target antibiotic; If the bacteria in the sensitive test group grow and the bacteria in the intermediate test group do not grow, the bacteria are determined to be intermediate strains of the target antibiotic; If the bacteria in both the sensitive test group and the intermediate test group grow, it is determined that the bacteria are resistant strains of the target antibiotic.

[0018] In the present application, the antibiotic is selected from at least one of β-lactams, aminoglycosides, macrolides, tetracyclines, quinolones, sulfonamides, glycopeptides, lincosamides, nitroimidazoles and polypeptide antibiotics.

[0019] Among them, β-lactams include penicillins, cephalosporins and carbapenems. Penicillins include but are not limited to penicillin G, ampicillin and piperacillin; cephalosporins include but are not limited to cefazolin, ceftriaxone, ceftazidime and cefotaxime; carbapenems include but are not limited to imipenem, meropenem and ertapenem.

[0020] Aminoglycosides include, but are not limited to, gentamicin, tobramycin, and amikacin.

[0021] Macrolides include, but are not limited to, erythromycin, azithromycin, and clarithromycin.

[0022] Tetracyclines include, but are not limited to, tetracycline, doxycycline, and minocycline.

[0023] Quinolones include, but are not limited to, Ciprofloxacin, Levofloxacin, and Moxifloxacin.

[0024] Sulfonamides include but are not limited to Sulfamethoxazole / Trimethoprim (SMZ-TMP).

[0025] Glycopeptides include, but are not limited to, vancomycin and teicoplanin.

[0026] Lincosamides include, but are not limited to, clindamycin and lincomycin.

[0027] Nitroimidazoles include but are not limited to metronidazole.

[0028] Peptides include but are not limited to Polymyxin B and Colistin.

[0029] Of course, those skilled in the art can also apply the present invention to the drug sensitivity detection of other antibiotics such as Linezolid, Daptomycin, Tigecycline and Fusidic acid.

[0030] The present application provides a method and device for rapid drug sensitivity detection of biological samples containing low-abundance bacteria. Through single-bacteria dynamic scattering imaging technology, direct and real-time detection of bacterial growth is achieved, without the need for traditional bacterial culture, separation and enrichment steps, and the detection time is significantly shortened from the traditional 2-5 days to a few hours, providing guidance for rapid clinical drug decision-making; the use of single-bacteria dynamic scattering imaging technology can accurately identify and count individual bacteria, monitor bacterial growth in real time, and improve the sensitivity and accuracy of detection; combined with the growth inhibition model to calculate the effective growth rate, it can more accurately judge the growth inhibition state and drug resistance of bacteria.

[0031] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become readily understood. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-limiting manner, wherein: In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0033] Figure 1 A flow chart showing a method for rapid drug sensitivity detection of biological samples containing low-abundance bacteria provided in Example 2 of the present application is shown; Figure 2 A flow chart of processing an original single bacteria dynamic light scattering image provided in Example 2 of the present application is shown; Figure 3A schematic diagram showing the growth inhibition curve of Escherichia coli to multiple antibiotics provided in Example 2 of the present application; Figure 4 A schematic diagram showing the growth inhibition results of a clinical positive blood culture sample provided in Example 2 of the present application at two breakpoint concentrations of multiple antibiotics; Figure 5 A schematic diagram of a receiver operating characteristic curve provided in Example 2 of the present application is shown; Figure 6 A comparison chart of the clinical VITEK 2 results provided in Example 2 of the present application is shown; Figure 7 A structural diagram of a rapid drug sensitivity detection device for biological samples containing low-abundance bacteria provided in Example 3 of the present application is shown. DETAILED DESCRIPTION

[0034] In order to make the purpose, features, and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0035] In order to solve the above-mentioned problems existing in the prior art, the embodiments of the present application provide a method for detecting the bacterial growth status of a biological sample containing low-abundance bacteria, a method for measuring the minimum inhibitory concentration of a target antibiotic, and a method and device for rapid drug sensitivity detection of a biological sample containing low-abundance bacteria, which are described in detail below.

[0036] Embodiment 1: Embodiment 1 of the present application provides a method for detecting the bacterial growth state of a biological sample containing low-abundance bacteria, comprising the following steps: Obtain biological samples containing low-abundance bacteria, where low abundance ranges from greater than or equal to 1 CFU / mL to less than or equal to 10 5 CFU / mL; The biological samples are divided into two groups and added into containers containing culture medium. The target antibiotic is added into the first group, which is used as the test group, and the antibiotic is not added into the second group, which is used as the control group. At each preset time, single bacteria scattering imaging is used to obtain image data in all first group of cuvettes to count bacteria; wherein the first group of cuvettes includes a first cuvette and a second cuvette, the first cuvette contains a test group, and the second cuvette contains a control group; When the predetermined recording time is reached, the ratio of the effective growth rate of the first cuvette to that of the second cuvette is calculated, and the growth inhibition state of the bacteria is determined in combination with the inhibition threshold.

[0037] In the embodiments of the present application, the method can be applied to the rapid drug sensitivity detection in the subsequent embodiments as one of the necessary links.

[0038] Embodiment 2: like Figure 1 As shown, Figure 1 A flowchart of a method for rapid drug sensitivity detection of a biological sample containing low-abundance bacteria provided in Example 2 of the present application, the method comprising the following steps: Obtain biological samples containing low-abundance bacteria, where low abundance ranges from greater than or equal to 1 CFU / mL to less than or equal to 10 5 CFU / mL.

[0039] When obtaining biological samples of bacterial infection, the bacterial concentration range is generally greater than or equal to 1 CFU / mL. 3 CFU / mL biological samples, such as blood samples, need to be pre-cultured for 3.5 hours before pretreatment and then perform drug sensitivity testing; the concentration range is greater than or equal to 10 3 Biological samples with low CFU / mL, such as positive blood culture samples, can be directly tested for drug sensitivity after simple pretreatment.

[0040] The concentration of antibiotics for drug sensitivity testing can be selected according to needs. If you want to obtain the minimum inhibitory concentration (MIC) of an antibiotic, you can dilute the target antibiotic in a gradient concentration and add them to containers containing culture medium. Then add a blank control group and test the growth inhibition of each group to obtain the MIC of the target antibiotic.

[0041] Specifically, the following steps are included: Obtain biological samples containing low-abundance bacteria, where low abundance ranges from greater than or equal to 1 CFU / mL to less than or equal to 10 5 CFU / mL; The biological samples are divided into two groups and added into containers containing culture medium respectively, wherein the first group comprises a plurality of containers, and different concentrations of target antibiotics are added into different containers respectively, serving as the test group, and no antibiotics are added into the second group, serving as the control group; At preset time intervals, single bacteria scattering imaging is used to obtain image data in all second group of cuvettes to count bacteria, wherein the second group of cuvettes includes a third cuvette and a fourth cuvette, the third cuvette contains a test group, and the fourth cuvette contains a control group; When the predetermined recording time is reached, the ratio of the effective growth rate of the third cuvette to that of the fourth cuvette is calculated, and the growth inhibition state of the bacteria is determined in combination with the inhibition threshold; From the target antibiotic concentrations corresponding to the test group in which the bacteria are in the inhibition state, the minimum value is selected as the minimum inhibitory concentration of the target antibiotic. In this way, the MIC value of the target antibiotic can be obtained.

[0042] If you want to obtain the antibiotic sensitivity classification results, you don't need too many concentration gradients. You can just divide the biological samples into three groups: sensitive, intermediate and blank.

[0043] Specifically, the biological samples are divided into three groups and added into containers containing culture medium respectively. The target antibiotic is added to the first group until the final concentration of the antibiotic is the sensitive breakpoint concentration, which is used as the sensitive test group. The target antibiotic is added to the second group until the final concentration of the antibiotic is the intermediate breakpoint concentration, which is used as the intermediate test group. No antibiotic is added to the third group, which is used as the control group.

[0044] At preset time intervals, single bacteria scattering imaging is used to obtain image data in all third group of cuvettes to count bacteria, wherein the third group of cuvettes includes a fifth cuvette and a sixth cuvette, the fifth cuvette contains a sensitive test group and an intermediate test group, and the sixth cuvette contains a control group.

[0045] When the predetermined recording time is reached, the ratio of the effective growth rate of the fifth cuvette to that of the sixth cuvette is calculated, and the growth inhibition state of the bacteria is determined in combination with the inhibition threshold.

[0046] Based on the bacterial growth in the sensitive test group and the intermediate test group, the bacterial resistance to the target antibiotic was determined.

[0047] In this embodiment, the above-mentioned biological samples containing low-abundance bacteria are all taken as blood infection samples as an example, but this is not limited to this. The biological samples containing low-abundance bacteria can include nasopharyngeal swab samples, oral swab samples, sputum samples, alveolar lavage fluid samples, pleural effusion samples, ascites samples, cerebrospinal fluid samples, serum samples, plasma samples, blood samples, urine samples, reproductive tract secretion samples, and stool samples, which can be selected according to actual conditions.

[0048] The above biological samples can be divided into two categories: sterile samples under normal circumstances, such as serum samples, plasma samples, blood samples, pleural effusion samples, ascites samples, joint fluid samples, hydrocele samples, bile samples, cerebrospinal fluid samples, and urine samples. These samples should be free of bacteria in a healthy human body. If bacteria are detected, it indicates infection and can be directly tested for rapid drug sensitivity after pretreatment; and samples that need to be cultured and isolated, such as sputum and feces. After culture and isolation, a single colony is picked and resuspended in culture broth, and then a rapid drug sensitivity test is performed. For example, sputum contains normal oral / respiratory flora, and pathogenic bacteria (such as Streptococcus pneumoniae) need to be cultured and distinguished; feces contain a large number of intestinal symbiotic bacteria, and pathogenic bacteria such as Salmonella and Shigella need to be selectively cultured and isolated.

[0049] A blood sample containing bacteria is obtained in advance, and the blood sample is pre-treated to remove impurities to obtain a bacterial sample.

[0050] First, obtain a blood sample infected with bacteria. This sample can be an original blood sample or a positive blood culture sample. For the original blood sample, ensure that the collection process is aseptic to avoid external infection.

[0051] For the original blood sample, since the pathogen content may be extremely low and there are a large number of blood cells and other impurities, a series of pretreatment steps are required to remove blood cells, lyse the remaining blood cells, remove impurities in the supernatant, and use red blood cell lysis buffer (such as ACK Lysis Buffer, Triton, etc.) and detergents (such as Tween 80, Tween 20, IGEPAL, Solutol HS 15, DSPE, etc.) to optimize the sample for better detection of pathogens. In addition, it is also necessary to control the particle concentration in the sample through steps such as dilution and filtration to ensure the accuracy of subsequent detection. Specifically, the following steps are included: After pre-incubation for 3.5 hours, approximately 1 mL of the collected original blood sample was placed in a centrifuge and centrifuged at 100 g for 5 minutes to separate blood cells and plasma.

[0052] The upper plasma layer after centrifugation was transferred to another sterile centrifuge tube, 1 mL of red blood cell lysis buffer was added, and after gentle mixing, it was incubated at 37°C for 2 minutes to lyse the remaining blood cells, and then centrifuged at 900g for 6 minutes, and the supernatant containing the lysate was removed.

[0053] Add 1 mL of detergent, incubate at 37°C for 2 minutes, and then centrifuge at 900g for 6 minutes and remove the supernatant to further remove impurities.

[0054] Add 1 mL of culture broth, filter the bacterial suspension with a 5 μm pinhole filter to remove large particles and incompletely lysed cell fragments, then centrifuge at 1000 g for 6 minutes and remove the supernatant to obtain a pure bacterial sample.

[0055] In order to effectively dissolve the cell membrane without causing excessive damage to the sample, the red blood cell lysis solution and detergent need to be diluted in culture broth to a concentration of 1% in advance.

[0056] For positive blood culture samples, the pretreatment steps are relatively simple because the pathogens have already multiplied in large numbers and impurities such as blood cells have mostly been removed or degraded during the culture process. The main purpose is to remove most of the blood cells (if any remain) and dilute the sample to control the concentration of the pathogen so that it is suitable for rapid drug sensitivity testing. Specifically, the following steps are included: Positive blood culture samples were directly placed in a centrifuge at 200 g for 6 minutes to remove blood cells and other non-bacterial components.

[0057] The upper part of the clear liquid after centrifugation was taken as the bacterial sample without further lysis treatment.

[0058] If the bacterial concentration in the bacterial sample is high, it is diluted in gradients using culture broth (such as CAMHB culture broth) and measured using a large-field scattering imaging system, and the suspension that meets the preset bacterial concentration range is selected as the drug sensitivity sample.

[0059] The CAMHB culture broth needs to be filtered by a 0.22 μm pinhole filter to prevent it from being interfered by impurities. This step is to ensure that the culture medium (CaMHB broth) used in the experiment is pure and free of impurities, so as to avoid interference from these impurities in the subsequent scattering imaging process, affecting the accurate tracking of bacterial growth and the judgment of drug sensitivity. By using a 0.22 μm pinhole filter, large impurities and microbial contaminants in the culture medium can be effectively removed.

[0060] In the present embodiment, the initial particle concentration is set to be less than 2×10 5 Particles / mL. This step is to adjust the bacterial concentration in the pretreated sample to a range suitable for drug sensitivity testing. By diluting the concentration gradient and counting with a large field of view scattering microscopy imaging system, it can be ensured that the number of bacteria in the drug sensitivity sample is moderate, neither too dense nor too sparse, which is convenient for subsequent growth tracking and drug sensitivity analysis.

[0061] The selected biological samples were divided into three groups. Different concentrations of target antibiotics were added to the first and second groups respectively, so that the final concentrations of the antibiotics were the sensitive breakpoint concentration and the intermediate breakpoint concentration. After mixing evenly, about 100 μL was taken and added to different fifth cuvettes.

[0062] The third group was set as the control group, i.e., the drug-sensitive samples without antibiotics, which were added to the sixth cuvette.

[0063] The above sensitive breakpoint concentrations and intermediate breakpoint concentrations are pre-set. According to the antibiotic breakpoint information of the Clinical and Laboratory Standards Institute (CLSI) of the United States, the breakpoint information of the target antibiotic is determined. The sensitivity of bacteria to a certain antibiotic is defined by the breakpoint, which is divided into sensitive (Susceptible, S), intermediate (Intermediate, I) and resistant (Resistant, R). Sensitive refers to isolates with a minimum inhibitory concentration (MIC) equal to or lower than the sensitive breakpoint. When the recommended dose is used to treat the infection site, the usually achievable concentration of antimicrobial drugs will inhibit them, thereby producing possible clinical efficacy; intermediate refers to isolates with MIC in the middle range of the usually achievable range, or isolates whose response rate may be lower than that of sensitive isolates; resistant refers to isolates with MIC higher than the resistant breakpoint, which cannot be inhibited by the drug concentration that can be usually achieved under normal dosage conditions.

[0064] Antibiotic susceptibility results are determined according to the CLSI (Clinical Laboratory Standards Institute) susceptibility classification rules. By testing and comparing the growth / inhibition curves of antibiotics at two breakpoint concentrations (sensitive breakpoint is low concentration, intermediate breakpoint is high concentration), the susceptibility test results are divided into three categories: susceptible, intermediate and resistant.

[0065] All the third group of cuvettes are placed in the cuvette holder in turn, ready for image acquisition. Set the camera exposure time to 3600 μs, set the frame rate to 5 fps, and shoot continuously for 5 s as the bacterial image data at 0 min (i.e., the initial moment). The position of the cuvette can be automatically switched by the electric control switching module, and the image data in each third group of cuvettes can be recorded in turn, which can save time and improve detection efficiency.

[0066] In order to observe the growth of bacteria at different time points, it is necessary to record the image data of all the third set of cuvettes at a certain time interval (such as 20 minutes). Through single bacterial scattering imaging and recognition counting technology, the bacteria in the image can be accurately identified and counted, thereby tracking the growth of bacteria in real time. This step is the core part of drug sensitivity testing, which provides information on the growth kinetics of bacteria under different antibiotic concentrations.

[0067] like Figure 2 As shown, Figure 2A flowchart for processing an original single bacterial dynamic light scattering image provided in Example 2 of the present application. Using a large field of view scattering microscopy imaging system, all third group of cuvettes are subjected to large field of view scattering imaging every 20 minutes, and the original single bacterial dynamic light scattering image sequence is recorded, i.e. Figure 2 As shown in A. Assuming that the image is recorded for 5 s at a frame rate of 5 frames per second, a total of 25 original single-bacteria dynamic light scattering images are obtained, constituting an original single-bacteria dynamic light scattering image sequence.

[0068] The large-field scattering microscopic imaging system used in the embodiment of the present invention adopts a forward dark-field scattering imaging structure to minimize background incident light interference and perform high-sensitivity single-cell resolution imaging; a large-field imaging is performed by combining a lens and a telephoto zoom lens to increase the imaging depth of field and axial resolution, so that the effective imaging volume is not less than 10 μL, achieving 10 3 The number of pathogens directly imaged in the actual sample was no less than 10 CFU / mL.

[0069] During dynamic light scattering imaging, vibration noise may be introduced into the image sequence due to the instability of the equipment or environment, which will affect the accuracy of subsequent bacterial identification. The time domain difference method identifies and removes areas with large intensity changes caused by vibration by comparing the image differences of adjacent frames. Specifically, it calculates the difference between two adjacent frames and removes areas in the difference image where the intensity exceeds a certain threshold, thereby effectively eliminating vibration noise.

[0070] First, calculate the difference between two adjacent frames of images to obtain a differential image. Each pixel value in the differential image represents the intensity change of the pixel at the corresponding position in the original image between the two frames. Then, set an intensity threshold. For each pixel in the differential image, if its intensity value exceeds this threshold, it is considered that the pixel belongs to a region of significant change caused by vibration or other reasons. For areas in the differential image where the intensity exceeds the threshold, they can be directly ignored, that is, the two frames of original single bacteria dynamic light scattering images corresponding to the differential image are deleted from the original single bacteria dynamic light scattering image sequence. For example, if the differential image is obtained by differentiating the 10th and 11th frames, and the intensity in the differential image exceeds the intensity threshold, the 10th and 11th frames of original single bacteria dynamic light scattering images are deleted to eliminate the influence of vibration noise and obtain the first original image sequence, that is, Figure 2 As shown in B (the sequence is not shown, only one of the pictures in the sequence is used as an example).

[0071] By calculating the temporal local minimum value of each pixel in the first original image sequence, a background image is constructed, and the background image is subtracted frame by frame from the first original image sequence to remove static noise and slow drift noise, thereby obtaining the second original image sequence, namely: Figure 2As shown in C (the sequence is not shown, only one of the pictures in the sequence is used as an example).

[0072] Static noise usually refers to the fixed or minimally changing parts of the image, while drift noise is caused by slowly moving or unstable factors. These noises can be identified and removed by calculating the minimum intensity of each pixel over a short period of time (i.e., the local minimum in the time domain).

[0073] Setting a smaller stack size ensures that static and slow drift noise are removed while retaining faster-moving bacterial signals to avoid signal loss. When the stack size increases, although it contains more frame information, it also increases the risk of including other dynamic changes that are not related to the current pixel. These dynamic changes may include the passage of other moving objects, changes in lighting conditions, camera shake, etc., which may interfere with the calculation of the local minimum in the time domain.

[0074] A stack refers to the range of frames used when calculating the local minimum in the time domain. If the stack is too large and contains dynamic changes that are unrelated to the current pixel, the calculated local minimum in the time domain may no longer simply reflect the level of background noise, but may be "contaminated" by these dynamic changes. In this case, the local minimum in the time domain may be overestimated because it contains additional, unnecessary intensity changes. When this "contaminated" local minimum image in the time domain is subtracted frame by frame from the original image sequence, a portion of the bacterial signal that should have been retained may be removed. This is because the removed portion may contain both background noise and part of the bacterial signal, and since the local minimum in the time domain is overestimated, the removed noise may also contain the intensity of the bacterial signal.

[0075] The spatial local background of each pixel in the second original image sequence is calculated by averaging a large area within a preset radius around the pixel, and the spatial local background is subtracted from the second original image sequence to remove the spatial background noise, thereby obtaining the third original image sequence, namely: Figure 2 As shown in D (the sequence is not shown, only one of the images in the sequence is used as an example), the radius is set to be no less than the radius of the largest target detection object.

[0076] Spatial noise refers to intensity variations that are randomly distributed in the image and are unrelated to the bacterial signal. To remove this noise, spatial filtering methods such as local average filtering can be used. By averaging a large area within a certain radius around each pixel, the spatial local background of the pixel can be calculated and subtracted from the second original image sequence to remove the spatial background noise. The radius should be set to ensure that the largest target detection object can be covered to avoid mistakenly removing bacterial signals as background.

[0077] Image processing algorithms and filters are used to identify individual bacteria of different morphologies in the third original image sequence, and a single bacteria scattered light spot image sequence is obtained, namely Figure 2 As shown in E (the sequence is not shown, only one of the pictures in the sequence is used as an example).

[0078] The above image processing algorithm can use the watershed algorithm, and the filter can use the Gaussian Laplace operator. The watershed algorithm is a segmentation method based on image morphology. It simulates the concept of watershed in topography and is used to divide continuous areas in an image into different parts. Here, it combines the Gaussian Laplace operator to accurately identify individual bacteria with different morphologies. The Gaussian Laplace operator can highlight the edge information in the image, and the watershed algorithm separates the bacteria from the background based on the edge information.

[0079] After filtering the single bacteria scattered light spot image to remove abnormal points, count each single bacteria scattered light spot image in the single bacteria scattered light spot image sequence and calculate the sum of the numbers. The sum of the numbers is divided by the number of single bacteria scattered light spot image sequences to obtain the number of single bacteria, which is plotted as a time curve, as shown in Figure 2 As shown in F.

[0080] After identifying individual bacteria, the detected scattered light spots need to be further filtered to remove abnormal points (such as noise, artifacts, etc.). This can be achieved by applying filters such as minimum, signal-to-noise ratio, and standard deviation. Finally, count each filtered scattered light spot image, find the sum of the number, and divide it by the number of images to obtain the number of all single bacteria in the field of view.

[0081] When the predetermined recording time is reached, the ratio of the effective growth rate of the fifth cuvette to that of the sixth cuvette is calculated, and the growth inhibition state of the bacteria is determined in combination with the inhibition threshold.

[0082] The scheduled recording time can be set to 120 minutes, which can be set according to actual conditions, and the embodiment of the present application does not limit this.

[0083] The effective growth rate was calculated based on the growth inhibition model.

[0084] The model for changes in bacterial populations in the presence of commonly used antibiotics is as follows:

[0085] in, is the change in bacterial population over time; k g (min -1 ) is the bacterial growth rate constant in the absence of antibiotics; k d (min-1 ) is the maximum killing rate constant; EC 50 (μg mL -1 ) is the antibiotic concentration required to produce 50% of the maximum bactericidal effect; C (μg mL -1 ) is the antibiotic concentration at a certain time (t); N is the number of surviving bacteria.

[0086] For simplicity, the concentration of antibiotics is assumed to remain constant and effective during the time of the rapid test (C ≫ EC 50 In addition, due to the complexity of blood infection samples, which may contain a small number of cells or platelets, N 0 Corrected as an impurity term: ; In the initial stage, bacteria may not be in the logarithmic growth phase, so the delay in the effectiveness of antibiotics must be considered. To compensate for these delays, the above model is adjusted by adding a correction factor (1-exp -rt ). Since the delays in bacterial growth and antibiotic killing are not necessarily equal, the growth delay is calculated as 1-exp -αt Calculated, and the killing delay is 1-exp -βt calculate: ; Through the above model, we can calculate and fit t The number of bacterial cells in the system at the moment: ; Effective growth rate K growth Can be used to quantify effective bacterial growth: 。

[0087] After continuously recording image data for 120 minutes, a growth / killing time curve can be drawn based on the counting results of single bacterial cells. These curves are then analyzed and compared using the above growth inhibition analysis model to obtain the effective growth rate ratio between the test group and the control group, which is then compared with the inhibition threshold to determine the sensitivity of the bacteria to the target antibiotic. Based on the growth of bacteria at different antibiotic concentrations, they can be divided into three categories: sensitive, intermediate or resistant, thus providing important reference information for clinical treatment.

[0088] like Figure 3 As shown, Figure 3A schematic diagram of the growth inhibition curve of Escherichia coli against multiple antibiotics provided in Example 2 of the present application. Among them, w / o represents the blank group, AMK is amikacin, CIP is ciprofloxacin, CX is ceftriaxone, ETP is ertapenem, PB is polymyxin, TGC is polymyxin, and TZP is piperacillin / tazobactam. The effective growth rate ratio of bacteria at the concentrations of the sensitive breakpoints (Low) and intermediate breakpoints (High) of the above 7 antibiotics was detected. K growth-ABX / K growth-C (Effective growth rate of antibiotic group / effective growth rate of blank group) to determine its sensitivity to antibiotics. The test results are shown in Table 1 below.

[0089] Table 1 Results of drug sensitivity test of 7 antibiotics

[0090] In the table, LVSi-AST is the test result obtained by the method provided in the embodiment of the present application. In the embodiment of the present application, the inhibition threshold is set to 0.09. Taking AMK as an example, the effective growth rate ratio of bacteria at two concentrations is lower than 0.09, and the bacterial growth is inhibited, then the AST (Antimicrobial Susceptibility Testing, drug sensitivity test) result is S; CIP, the effective growth rate ratio is greater than 0.09 at the sensitive concentration, the bacteria are not inhibited, and the effective growth rate ratio is less than 0.09 at the intermediate concentration, the bacterial growth is inhibited, then the AST result is I; CX, the effective growth rate of bacteria at two concentrations is greater than 0.09, and the bacterial growth is not inhibited, then the AST result is R. The drug sensitivity test results are consistent with the results of the commonly used commercial instrument VITEK 2 in clinical practice, and the gold standard method E-test method, wherein the E-test method can obtain MIC results.

[0091] like Figure 4 As shown, Figure 4 This is a schematic diagram of the growth inhibition results of a clinical positive blood culture sample at two breakpoint concentrations of multiple antibiotics provided in Example 2 of the present application.

[0092] A total of 200 tests were performed on 16 clinically positive blood culture samples at two breakpoint concentrations for multiple antibiotics. With the inhibition threshold set at 0.09, 159 tests were classified as inhibition and 41 tests were classified as growth within 120 minutes, with a concordance accuracy (concordant samples / total samples, compared with clinical VITEK 2 results) of 98%.

[0093] like Figure 5 As shown, Figure 5 This is a schematic diagram of a receiver operating characteristic curve provided in Example 1 of the present application.K growth-ABX / K growth-C The receiver operating characteristic (ROC) curves obtained for the prediction parameters further validated the classification results, with a sensitivity of 95.1% and a specificity of 98.7% at 120 minutes.

[0094] like Figure 6 As shown, Figure 6 A comparison chart with clinical VITEK 2 results provided in Example 1 of the present application. 100 AST results were compared with clinical VITEK 2 results, and the classification consistency rate reached 96%, with no major errors or very major errors.

[0095] In the examples of the present application, we used two breakpoint concentrations to improve the accuracy of the drug sensitivity test, and in most cases, sensitive and intermediate breakpoints were selected. According to the CLSI M100 guidelines, for samples classified as intermediate to antibiotics, MIC values ​​are usually closely related to the intermediate breakpoint concentrations, which makes them highly sensitive to small changes in the accuracy of the prepared antibiotic concentrations. The differences observed in the intermediate classification may be related to these small fluctuations in antibiotic concentrations in different tests. However, according to the CLSI M52 guidelines, such small errors are considered acceptable in the context of clinical diagnosis.

[0096] The embodiment of the present application provides a method for rapid drug sensitivity detection of biological samples containing low-abundance bacteria. Through the large-field scattering imaging technology, direct and real-time detection of bacterial growth is achieved, without the need for traditional bacterial culture, separation and enrichment steps, and the detection time is significantly shortened from the traditional 2-5 days to a few hours, which provides the possibility for rapid clinical decision-making; the single-bacteria dynamic scattering imaging technology is used to accurately identify and count individual bacteria, monitor bacterial growth in real time, and improve the sensitivity and accuracy of detection; combined with the growth inhibition model to calculate the effective growth rate, it can more accurately judge the growth inhibition state and drug resistance of bacteria; it is not only applicable to original blood samples, but also to positive blood culture samples, and has a wide range of applicability. In addition, due to the use of standardized operating procedures and data processing methods, it is easy to promote and apply between different clinics and laboratories; by quickly and accurately detecting the drug sensitivity of bacteria, it can provide timely guidance on the use of antibiotics for the clinic, avoid the drug resistance problem caused by blind use of antibiotics, and improve the treatment effect and patient survival rate.

[0097] Embodiment three: like Figure 7 As shown, Figure 7 This is a structural diagram of a rapid drug sensitivity detection device for biological samples containing low-abundance bacteria provided in Example 3 of the present application. The device includes: The acquisition module is used to obtain biological samples containing low-abundance bacteria. The low abundance range is greater than or equal to 1 CFU / mL and less than or equal to 10 5 CFU / mL; A grouping module is used to divide the biological samples into three groups, and add them into containers containing culture medium respectively, wherein the target antibiotic at a sensitive breakpoint concentration is added to the first group as a sensitive test group, the target antibiotic at an intermediate breakpoint concentration is added to the second group as an intermediate test group, and no antibiotic is added to the third group as a control group; A counting module, used for obtaining image data in all the third group of cuvettes by single bacteria scattering imaging at preset time intervals to count bacteria, wherein the third group of cuvettes includes a fifth cuvette and a sixth cuvette, the fifth cuvette includes a sensitive test group and an intermediate test group, and the sixth cuvette includes a control group; A calculation module, used to calculate the ratio of the effective growth rate of the fifth cuvette to that of the sixth cuvette when the predetermined recording time is reached, and determine the growth inhibition state of the bacteria in combination with the inhibition threshold; The determination module is used to determine the bacterial resistance to the target antibiotic based on the bacterial growth conditions of the sensitive test group and the intermediate test group.

[0098] Specifically, the counting module includes: A differential calculation unit is used to perform single bacterial scattering imaging on all the third group of cuvettes at preset time intervals, record multiple original single bacterial dynamic light scattering image sequences, and perform differential image differentiation on adjacent frame images to obtain differential images, wherein each pixel value in the differential image represents the intensity change of the pixel at the corresponding position in the original single bacterial dynamic light scattering images of two adjacent frames; if the intensity in the differential image exceeds the intensity threshold, then remove the related images of the differential image from the original single bacterial dynamic light scattering image sequence to obtain a first original image sequence; A time domain local minimum value calculation unit, used to construct a background image by calculating the time domain local minimum value of each pixel in the first original image sequence, and subtract the background image from the first original image sequence frame by frame to obtain a second original image sequence; a spatial local background calculation unit, configured to calculate the spatial local background of each pixel in the second original image sequence by averaging a large area within a preset radius around the pixel, and to subtract the spatial local background from the second original image sequence to obtain a third original image sequence, wherein the radius is set to be no less than the radius of the largest target detection object; an identification unit, used for identifying individual bacteria of various shapes in the third original image sequence by using an image processing algorithm and a filter, and obtaining a single bacteria scattered light spot image sequence; The filtering and counting unit is used to filter the single bacteria scattered light spot image sequence to remove abnormal points, count each single bacteria scattered light spot image in the single bacteria scattered light spot image sequence and obtain the sum of the numbers, and divide the sum of the numbers by the number of single bacteria scattered light spot image sequences to obtain the number of single bacteria.

[0099] The rapid drug sensitivity detection device for biological samples containing low-abundance bacteria provided in Example 3 of the present application has the same implementation principle and technical effects as those of the aforementioned method Example 2. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding contents in the aforementioned method Example 2.

[0100] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0101] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0102] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for detecting the bacterial growth state of a biological sample containing low-abundance bacteria, characterized in that: The following steps are involved: Obtain a biological sample containing low-abundance bacteria, where the low-abundance range is greater than or equal to 1 CFU / mL and less than or equal to 10 5 CFU / mL; The biological samples are divided into two groups and added into containers containing culture medium. The target antibiotic is added into the first group, which is used as the test group, and the antibiotic is not added into the second group, which is used as the control group. At each preset time, the image data in all the first group of cuvettes are obtained by single bacteria scattering imaging to count the bacteria; wherein the first group of cuvettes includes a first cuvette and a second cuvette, the first cuvette contains a test group, and the second cuvette contains a control group; When the predetermined recording time is reached, the ratio of the effective growth rate of the first cuvette to the second cuvette is calculated, and the growth inhibition state of the bacteria is determined in combination with the inhibition threshold.

2. A method for measuring the minimum inhibitory concentration of a target antibiotic in a biological sample containing low-abundance bacteria, characterized in that: The following steps are involved: Obtain a biological sample containing low-abundance bacteria, where the low-abundance range is greater than or equal to 1 CFU / mL and less than or equal to 10 5 CFU / mL; Dividing the biological samples into two groups and adding them into containers containing culture medium respectively, wherein the first group comprises a plurality of the containers, and adding different concentrations of target antibiotics into different containers respectively, as the test group, and the second group does not add antibiotics, as the control group; At preset time intervals, single bacteria scattering imaging is used to obtain image data in all second group of cuvettes to count bacteria, wherein the second group of cuvettes includes a third cuvette and a fourth cuvette, the third cuvette contains a test group, and the fourth cuvette contains a control group; When the predetermined recording time is reached, the ratio of the effective growth rate of the third cuvette to that of the fourth cuvette is calculated, and the growth inhibition state of the bacteria is determined in combination with the inhibition threshold; From the target antibiotic concentrations corresponding to the test group in which bacteria are in an inhibited state, the minimum value is selected as the minimum inhibitory concentration of the target antibiotic.

3. A method for rapid drug sensitivity detection of biological samples containing low-abundance bacteria, characterized in that: The following steps are involved: Obtain a biological sample containing low-abundance bacteria, where the low-abundance range is greater than or equal to 1 CFU / mL and less than or equal to 10 5 CFU / mL; The biological samples are divided into three groups and added into containers containing culture medium respectively, wherein the target antibiotic is added into the first group until the final concentration of the antibiotic is the sensitive breakpoint concentration, which is used as a sensitive test group, the target antibiotic is added into the second group until the final concentration of the antibiotic is the intermediate breakpoint concentration, which is used as an intermediate test group, and no antibiotic is added into the third group, which is used as a control group; At preset time intervals, single bacteria scattering imaging is used to obtain image data in all third group of cuvettes to count bacteria, wherein the third group of cuvettes includes a fifth cuvette and a sixth cuvette, the fifth cuvette includes a sensitive test group and an intermediate test group, and the sixth cuvette includes a control group; When the predetermined recording time is reached, calculating the ratio of the effective growth rate of the fifth cuvette to that of the sixth cuvette, and determining the growth inhibition state of the bacteria in combination with the inhibition threshold; Based on the bacterial growth conditions of the sensitive test group and the intermediate test group, the drug resistance of the bacteria to the target antibiotic is determined.

4. The method for rapid drug sensitivity detection of biological samples containing low-abundance bacteria according to claim 3, characterized in that: If the bacterial concentration of the biological sample is in the range of 1-10 3 CFU / mL, the biological sample is pre-cultured before being added to the container containing the culture medium; if the bacterial concentration range of the biological sample is greater than 10 5 CFU / mL, the biological sample is diluted before adding it to the container containing the culture medium.

5. The method for rapid drug sensitivity detection of biological samples containing low-abundance bacteria according to claim 3, characterized in that: The step of obtaining image data in all the third group of cuvettes by single bacteria scattering imaging at preset time intervals to count the bacteria comprises: At preset time intervals, single bacteria scattering imaging is performed on all cuvettes of the third group, and an original single bacteria dynamic light scattering image sequence is recorded. Adjacent frame images are differentiated to obtain a differential image, wherein each pixel value in the differential image represents an intensity change of a pixel at a corresponding position in two adjacent frames of the original single bacteria dynamic light scattering image; if the intensity in the differential image exceeds an intensity threshold, then the related images of the differential image are removed from the original single bacteria dynamic light scattering image sequence to obtain a first original image sequence; A background image is constructed by calculating the time domain local minimum value of each pixel in the first original image sequence, and the background image is subtracted frame by frame from the first original image sequence to obtain a second original image sequence; The spatial local background of each pixel in the second original image sequence is calculated by averaging a large area within a preset radius around the pixel, and the spatial local background is subtracted from the second original image sequence to obtain a third original image sequence, wherein the radius is set to be not less than the radius of the largest target detection object; Using image processing algorithms and filters to identify individual bacteria of various shapes in the third original image sequence, to obtain a single bacteria scattered light spot image sequence; After filtering the single bacteria scattered light spot image sequence to remove abnormal points, each single bacteria scattered light spot image in the single bacteria scattered light spot image sequence is counted and the sum of the numbers is obtained, and the sum of the numbers is divided by the number of single bacteria scattered light spot image sequences to obtain the number of single bacteria.

6. The method for rapid drug sensitivity detection of biological samples containing low-abundance bacteria according to claim 3, characterized in that: The biological samples containing low-abundance bacteria include nasopharyngeal swab samples, oral swab samples, sputum samples, bronchoalveolar lavage fluid samples, pleural effusion samples, ascites samples, cerebrospinal fluid samples, serum samples, plasma samples, blood samples, urine samples, reproductive tract secretion samples, and fecal samples.

7. The method for rapid drug sensitivity detection of biological samples containing low-abundance bacteria according to claim 3, characterized in that: The effective growth rate is calculated based on a growth inhibition model, which is: ； In the formula, is the change in bacterial population over time; is the bacterial growth rate constant in the absence of antibiotics; is the maximum killing rate constant; is the growth delay correction factor; is the kill delay correction factor; is the impurity term correction parameter; Based on the growth inhibition model, the number of bacterial cells at time t is calculated as: ； The effective growth rate is: .

8. The method for rapid drug sensitivity detection of biological samples containing low-abundance bacteria according to claim 3, characterized in that: The step of calculating the ratio of the effective growth rate of the fifth cuvette to that of the sixth cuvette when the predetermined recording time is reached, and determining the growth inhibition state of the bacteria in combination with the inhibition threshold, comprises: Calculating the ratio of the effective growth rate of bacteria in the fifth cuvette to that in the sixth cuvette; comparing the ratio to an inhibition threshold; If the ratio is lower than the inhibition threshold, the target antibiotic at this concentration has an inhibitory effect on bacterial growth, and the bacteria in the fifth cuvette are determined to be in an inhibition state; Otherwise, it is determined that the bacteria in the fifth cuvette are in a growth state.

9. The method for rapid drug sensitivity detection of biological samples containing low-abundance bacteria according to claim 3, characterized in that: The step of determining the drug resistance of bacteria to the target antibiotic based on the bacterial growth conditions of the sensitive test group and the intermediate test group comprises: Determining the bacterial growth conditions of the sensitive test group and the intermediate test group; If the bacteria in the sensitive test group and the intermediate test group do not grow, the bacteria are determined to be sensitive strains of the target antibiotic; If the bacteria in the sensitive test group grow and the bacteria in the intermediate test group do not grow, the bacteria are determined to be intermediate strains of the target antibiotic; If the bacteria in both the sensitive test group and the intermediate test group grow, it is determined that the bacteria are resistant strains of the target antibiotic.

10. A rapid drug sensitivity detection device for biological samples containing low-abundance bacteria, characterized in that: include: The acquisition module is used to acquire biological samples containing low-abundance bacteria, where the low-abundance range is greater than or equal to 1 CFU / mL and less than or equal to 10 5 CFU / mL; A grouping module, for dividing the biological sample into three groups, adding them into containers containing culture medium respectively, adding the target antibiotic into the first group until the final concentration of the antibiotic is the sensitive breakpoint concentration, serving as a sensitive test group, adding the target antibiotic into the second group until the final concentration of the antibiotic is the intermediate breakpoint concentration, serving as an intermediate test group, and not adding the antibiotic into the third group, serving as a control group; a counting module, configured to obtain image data in all third group of cuvettes by single bacteria scattering imaging at preset time intervals, and count bacteria, wherein the third group of cuvettes comprises a fifth cuvette and a sixth cuvette, the fifth cuvette comprises a sensitive test group and an intermediate test group, and the sixth cuvette comprises a control group; A calculation module, used for calculating the ratio of the effective growth rate of the fifth cuvette to that of the sixth cuvette when a predetermined recording time is reached, and determining the growth inhibition state of the bacteria in combination with an inhibition threshold; The determination module is used to determine the drug resistance of bacteria to the target antibiotic based on the bacterial growth conditions of the sensitive test group and the intermediate test group.

11. The rapid drug sensitivity detection device for biological samples containing low-abundance bacteria according to claim 10, characterized in that: The counting module comprises: A differential calculation unit is used to perform single bacteria scattering imaging on all the third group of cuvettes at preset time intervals, record multiple original single bacteria dynamic light scattering image sequences, and perform differential calculation on adjacent frame images to obtain differential images, wherein each pixel value in the differential image represents the intensity change of the pixel at the corresponding position in the original single bacteria dynamic light scattering images of two adjacent frames; if the intensity in the differential image exceeds an intensity threshold, then remove the related images of the differential image from the original single bacteria dynamic light scattering image sequence to obtain a first original image sequence; A time domain local minimum value calculation unit, configured to construct a background image by calculating the time domain local minimum value of each pixel in the first original image sequence, and to subtract the background image from the first original image sequence frame by frame to obtain a second original image sequence; a spatial local background calculation unit, configured to calculate the spatial local background of each pixel in the second original image sequence by averaging a large area within a preset radius around the pixel, and subtract the spatial local background from the second original image sequence to obtain a third original image sequence, wherein the radius is set to be not less than the radius of the largest target detection object; an identification unit, used for identifying individual bacteria of various shapes in the third original image sequence by using an image processing algorithm and a filter, and obtaining a single bacteria scattered light spot image sequence; A filtering and counting unit is used to filter the single bacteria scattered light spot image sequence to remove abnormal points, count each single bacteria scattered light spot image in the single bacteria scattered light spot image sequence and obtain the sum of the numbers, and divide the sum of the numbers by the number of images in the single bacteria scattered light spot image sequence to obtain the number of single bacteria.

12. The rapid drug sensitivity detection device for biological samples containing low-abundance bacteria according to claim 10, characterized in that: The biological samples containing low-abundance bacteria include nasopharyngeal swab samples, oral swab samples, sputum samples, bronchoalveolar lavage fluid samples, pleural effusion samples, ascites samples, cerebrospinal fluid samples, serum samples, plasma samples, blood samples, urine samples, reproductive tract secretion samples, and fecal samples.

13. The rapid drug sensitivity detection device for biological samples containing low-abundance bacteria according to claim 10, characterized in that: The effective growth rate is calculated based on a growth inhibition model, which is: ； In the formula, is the change in bacterial population over time; is the bacterial growth rate constant in the absence of antibiotics; is the maximum killing rate constant; is the growth delay correction factor; is the kill delay correction factor; is the impurity term correction parameter; Based on the growth inhibition model, the number of bacterial cells at time t is calculated as: ； The effective growth rate is: ; The step of calculating the ratio of the effective growth rate of the fifth cuvette to that of the sixth cuvette when the predetermined recording time is reached, and determining the growth inhibition state of the bacteria in combination with the inhibition threshold, comprises: Calculating the ratio of the effective growth rate of bacteria in the fifth cuvette to that in the sixth cuvette; comparing the ratio to an inhibition threshold; If the ratio is lower than the inhibition threshold, the target antibiotic at this concentration has an inhibitory effect on bacterial growth, and the bacteria in the fifth cuvette are determined to be in an inhibition state; Otherwise, determining that the bacteria in the fifth cuvette are in a growth state; The step of determining the drug resistance of bacteria to the target antibiotic based on the bacterial growth conditions of the sensitive test group and the intermediate test group comprises: Determining the bacterial growth conditions of the sensitive test group and the intermediate test group; If the bacteria in the sensitive test group and the intermediate test group do not grow, the bacteria are determined to be sensitive strains of the target antibiotic; If the bacteria in the sensitive test group grow and the bacteria in the intermediate test group do not grow, the bacteria are determined to be intermediate strains of the target antibiotic; If the bacteria in both the sensitive test group and the intermediate test group grow, it is determined that the bacteria are resistant strains of the target antibiotic.

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