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

Abstract

This application provides a method and device for rapid drug sensitivity detection of biological samples containing low-abundance bacteria, relating to the field of scattering imaging technology. Through single-bacteria dynamic scattering imaging technology, direct, real-time detection of bacterial growth is achieved, eliminating the need for traditional bacterial culture, separation, and enrichment steps. This significantly shortens the detection time from the traditional 2-5 days to a few hours, providing guidance for rapid clinical drug decision-making. Using single-bacteria dynamic scattering imaging technology, it is possible 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 determine the growth inhibition state and drug resistance of bacteria.

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] Currently, the main methods for bacterial drug susceptibility testing include genotypic and phenotypic methods. The former detects bacterial resistance genes, but can easily lead to false positive and false negative results if prior knowledge is insufficient or the gene is not expressed. The latter more directly measures phenotypic characteristics and is the gold standard drug susceptibility method. However, these pre-processing steps still require bacterial culture, isolation, and enrichment, which are very time-consuming.

[0003] Optical imaging methods are used for rapid bacterial drug susceptibility testing, which offers advantages such as visualization and real-time detection. However, while conventional optical microscopes can image bacteria, they require them to be fixed on a surface. Furthermore, the field of view of high-resolution optical microscopes is very small, requiring bacterial enrichment in low-concentration samples, resulting in low efficiency in bacterial drug susceptibility 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 targetedness 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 status of a biological sample containing low-abundance bacteria is provided, comprising the following steps:

[0006] 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;

[0007] The biological samples are divided into two groups and added into containers containing culture medium. The first group is added with the target antibiotic and serves as the test group, while the second group is not added with the antibiotic and serves as the control group.

[0008] At each preset time duration, single bacteria scattering imaging is used to obtain image data in all first group of cuvettes 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;

[0009] When the predetermined recording time is reached, the ratio of the effective growth rate of the first cuvette to the effective growth rate of the second cuvette is calculated, and the growth inhibition state of the bacteria is determined in combination with the inhibition threshold.

[0010] 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:

[0011] 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;

[0012] Dividing the biological samples into two groups and adding them to containers containing culture medium, respectively, wherein the first group comprises a plurality of said containers, and different concentrations of target antibiotics are added to different said containers, serving as the test group, and the second group does not contain antibiotics, serving as the control group;

[0013] At predetermined intervals, single bacteria scattering imaging is used to obtain image data from all second group of cuvettes to count the 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;

[0014] When the predetermined recording time is reached, calculating the ratio of the effective growth rate of the third cuvette to the effective growth rate of the fourth cuvette, and determining the growth inhibition state of the bacteria in combination with the inhibition threshold;

[0015] 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.

[0016] According to a 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:

[0017] 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;

[0018] The biological samples are divided into three groups and added to containers containing culture medium respectively. The target antibiotic is added to the first group until the final antibiotic concentration is the sensitive breakpoint concentration, which serves as a sensitive test group. The target antibiotic is added to the second group until the final antibiotic concentration is the intermediate breakpoint concentration, which serves as an intermediate test group. No antibiotic is added to the third group, which serves as a control group.

[0019] At predetermined intervals, single bacteria scatter imaging is used to obtain image data from all cuvettes in the third group, and the bacteria are counted, 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;

[0020] When the predetermined recording time is reached, calculating the ratio of the effective growth rate of the fifth cuvette to the effective growth rate of the sixth cuvette, and determining the growth inhibition state of the bacteria in combination with the inhibition threshold;

[0021] 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.

[0022] In one embodiment, 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.

[0023] In one embodiment, the step of obtaining image data in all third groups of cuvettes using single bacteria scattering imaging at predetermined intervals and counting bacteria includes:

[0024] At predetermined intervals, single bacterial scattering imaging is performed on all cuvettes in the third group, and a sequence of original single bacterial dynamic light scattering images is recorded. Adjacent frame images are differentiated to obtain differential images, 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 bacterial dynamic light scattering images; if the intensity in the differential image exceeds an intensity threshold, images related to the differential image are removed from the sequence of original single bacterial dynamic light scattering images to obtain a first sequence of original images;

[0025] A background image is constructed by calculating a temporal 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;

[0026] Calculating the spatial local background of each pixel by averaging a large area within a preset radius around each pixel in the second original image sequence, and subtracting 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;

[0027] using an image processing algorithm and a filter to identify individual bacteria of various morphologies in the third original image sequence to obtain a single bacteria scattered light spot image sequence;

[0028] 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.

[0029] 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 genital secretion sample, or a stool sample.

[0030] In one embodiment, the effective growth rate is calculated based on a growth inhibition model, and the growth inhibition model is:

[0031] ;

[0032] Where, 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 correction parameter;

[0033] Based on the growth inhibition model, the number of bacterial cells at time t is calculated as:

[0034] ;

[0035] The effective growth rate is: .

[0036] In one embodiment, the step of calculating the ratio of the effective growth rate of the fifth cuvette to the effective growth rate 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, includes:

[0037] Calculating the ratio of the effective growth rate of bacteria in the fifth cuvette to that in the sixth cuvette;

[0038] comparing the ratio to an inhibition threshold;

[0039] 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;

[0040] Otherwise, it is determined that the bacteria in the fifth cuvette are in a growth state.

[0041] In one embodiment, the step of determining the bacterial resistance to the target antibiotic based on the bacterial growth conditions of the sensitive test group and the intermediate test group includes:

[0042] Determining bacterial growth conditions of the sensitive test group and the intermediate test group;

[0043] 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;

[0044] 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;

[0045] 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.

[0046] 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:

[0047] The acquisition module is used to obtain 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;

[0048] a grouping module, configured to divide the biological samples into three groups, add the biological samples into containers containing culture medium respectively, add the target antibiotic to the first group until the final antibiotic concentration is the sensitive breakpoint concentration, and serve as the sensitive test group; add the target antibiotic to the second group until the final antibiotic concentration is the intermediate breakpoint concentration, and serve as the intermediate test group; and do not add the antibiotic to the third group, and serve as the control group;

[0049] a counting module, configured to obtain image data from all third group of cuvettes using 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 contains a sensitive test group and an intermediate test group, and the sixth cuvette contains a control group;

[0050] a calculation module, configured to calculate a ratio of the effective growth rate of the fifth cuvette to that of the sixth cuvette when a predetermined recording time is reached, and determine a growth inhibition state of the bacteria in combination with an inhibition threshold;

[0051] 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.

[0052] In one embodiment, the counting module includes:

[0053] a differential calculation unit, configured to perform single bacterial scattering imaging on all cuvettes of the third group at preset time intervals, record a plurality of original single bacterial 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 an intensity change of a pixel at a corresponding position in two adjacent frames of the original single bacterial dynamic light scattering images; and if the intensity in the differential image exceeds an intensity threshold, 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;

[0054] a temporal local minimum value calculation unit, configured to construct a background image by calculating the temporal 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;

[0055] 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 no less than the radius of the largest target detection object;

[0056] an identification unit, configured to identify individual bacteria of various morphologies in the third original image sequence using an image processing algorithm and a filter, and obtain a single bacteria scattered light spot image sequence;

[0057] 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 images in the single bacteria scattered light spot image sequence to obtain the number of single bacteria.

[0058] 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 genital secretion sample, or a stool sample.

[0059] In one embodiment, the effective growth rate is calculated based on a growth inhibition model, and the growth inhibition model is:

[0060] ;

[0061] Where, 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 correction parameter;

[0062] Based on the growth inhibition model, the number of bacterial cells at time t is calculated as:

[0063] ;

[0064] The effective growth rate is: ;

[0065] The step of calculating the ratio of the effective growth rate of the fifth cuvette to the effective growth rate 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:

[0066] Calculating the ratio of the effective growth rate of bacteria in the fifth cuvette to that in the sixth cuvette;

[0067] comparing the ratio to an inhibition threshold;

[0068] 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;

[0069] Otherwise, determining that the bacteria in the fifth cuvette are in a growth state;

[0070] The step of determining the bacterial resistance to the target antibiotic based on the bacterial growth conditions of the sensitive test group and the intermediate test group includes:

[0071] Determining bacterial growth conditions of the sensitive test group and the intermediate test group;

[0072] 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;

[0073] 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;

[0074] 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.

[0075] 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.

[0076] 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.

[0077] Aminoglycosides include, but are not limited to, gentamicin, tobramycin, and amikacin.

[0078] Macrolides include, but are not limited to, erythromycin, azithromycin, and clarithromycin.

[0079] Tetracyclines include, but are not limited to, tetracycline, doxycycline, and minocycline.

[0080] Quinolones include, but are not limited to, ciprofloxacin, levofloxacin, and moxifloxacin.

[0081] Sulfonamides include but are not limited to sulfamethoxazole / trimethoprim (SMZ-TMP).

[0082] Glycopeptides include, but are not limited to, vancomycin and teicoplanin.

[0083] Lincosamides include, but are not limited to, clindamycin and lincomycin.

[0084] Nitroimidazoles include but are not limited to metronidazole.

[0085] Peptides include but are not limited to polymyxin B and colistin.

[0086] Of course, those skilled in the art can also apply the present invention to drug sensitivity detection of other antibiotics such as linezolid, daptomycin, tigecycline and fusidic acid.

[0087] 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, significantly shortening the detection time from the traditional 2-5 days to a few hours, providing guidance for rapid clinical drug use decisions; 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 status and drug resistance of bacteria.

[0088] 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

[0089] The above and other objects, features and advantages of the exemplary embodiments of the present application will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present application are shown in an illustrative and non-limiting manner, in which:

[0090] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts.

[0091] 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;

[0092] Figure 2 A flowchart of processing an original single-bacteria dynamic light scattering image provided in Example 2 of the present application is shown;

[0093] Figure 3 A schematic diagram showing the growth inhibition curves of Escherichia coli against multiple antibiotics provided in Example 2 of the present application is shown;

[0094] Figure 4 A schematic diagram showing 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;

[0095] Figure 5A schematic diagram of a receiver operating characteristic curve provided in Example 2 of the present application is shown;

[0096] Figure 6 A comparison chart of the results of a clinical VITEK 2 test is shown in Example 2 of the present application;

[0097] 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

[0098] In order to make the purpose, features, and advantages of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of this application.

[0099] 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.

[0100] Example 1:

[0101] Example 1 of the present application provides a method for detecting the bacterial growth status of a biological sample containing low-abundance bacteria, comprising the following steps:

[0102] 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;

[0103] The biological samples are divided into two groups and added into containers containing culture medium. The first group is added with the target antibiotic and serves as the test group, while the second group is not added with the antibiotic and serves as the control group.

[0104] At each preset time duration, single bacterial scattering imaging is used to obtain image data in all first group of cuvettes 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;

[0105] 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.

[0106] In the examples of the present application, this method can be applied to the rapid drug sensitivity detection in subsequent examples as one of the necessary steps.

[0107] Example 2:

[0108] like Figure 1 As shown, Figure 1 This is a flow chart of a method for rapid drug sensitivity detection of biological samples containing low-abundance bacteria provided in Example 2 of the present application. The method includes the following steps:

[0109] 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.

[0110] 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 undergo 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.

[0111] The concentration of the antibiotic used in the susceptibility test can be selected based on the needs. To determine the minimum inhibitory concentration (MIC) of an antibiotic, the target antibiotic can be diluted in a gradient and added to containers containing culture medium. A blank control group can then be added to measure the growth inhibition of each group to determine the MIC of the target antibiotic.

[0112] Specifically, the following steps are included:

[0113] 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;

[0114] The biological samples are divided into two groups and added to containers containing culture medium. The first group includes multiple containers, and different concentrations of target antibiotics are added to different containers to serve as the test group. The second group does not contain antibiotics and serves as the control group.

[0115] At predetermined intervals, single bacteria scattering imaging is used to obtain image data from all second group of cuvettes to count the 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;

[0116] 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;

[0117] From the target antibiotic concentrations corresponding to the test group where the bacteria are in an inhibitory 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.

[0118] If you want to obtain the sensitivity classification results of antibiotics, you don’t need too many concentration gradients. You can just divide the biological samples into three groups: sensitive, intermediate and blank.

[0119] Specifically, the biological samples were divided into three groups and added into containers containing culture medium respectively. The target antibiotic was added to the first group until the final concentration of the antibiotic was the sensitive breakpoint concentration, which served as the sensitive test group. The target antibiotic was added to the second group until the final concentration of the antibiotic was the intermediate breakpoint concentration, which served as the intermediate test group. No antibiotic was added to the third group, which served as the control group.

[0120] At preset time intervals, single bacterial scattering imaging is used to obtain image data in all third group of cuvettes to count the bacteria, wherein the third group of cuvettes includes the fifth cuvette and the sixth cuvette, the fifth cuvette contains the sensitive test group and the intermediate test group, and the sixth cuvette contains the control group.

[0121] 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.

[0122] Based on the bacterial growth in the sensitive test group and the intermediate test group, the bacterial resistance to the target antibiotic is determined.

[0123] 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.

[0124] These biological samples can be divided into two categories: normally sterile samples, such as serum, plasma, blood, pleural effusion, ascites, joint fluid, hydrocele, bile, cerebrospinal fluid, and urine. These samples should be free of bacteria in a healthy individual, but the detection of bacteria indicates infection and can be directly tested for rapid drug sensitivity after pretreatment. The other types of samples require culture and isolation, such as sputum and stool. These samples require culture and isolation, followed by the selection of individual colonies, resuspending them in culture broth, and then undergoing rapid drug sensitivity testing. For example, sputum contains normal oral / respiratory flora and requires culture to distinguish pathogenic bacteria (such as Streptococcus pneumoniae); feces contains a large number of intestinal commensal bacteria and requires selective culture to isolate pathogenic bacteria such as Salmonella and Shigella.

[0125] A blood sample containing bacteria is obtained in advance, and the blood sample is pretreated to remove impurities to obtain a bacterial sample.

[0126] First, obtain a blood sample infected with bacteria. This sample can be a raw blood sample or a positive blood culture sample. For the raw blood sample, ensure that the collection process is aseptic to avoid external infection.

[0127] For raw blood samples, 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 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, steps such as dilution and filtration are required to control the particle concentration in the sample to ensure the accuracy of subsequent testing. Specifically, the following steps are included:

[0128] 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.

[0129] Transfer the upper plasma layer after centrifugation to another sterile centrifuge tube, add 1 mL of red blood cell lysis buffer, mix gently, incubate at 37°C for 2 minutes to lyse the remaining blood cells, then centrifuge at 900g for 6 minutes and remove the supernatant containing the lysis product.

[0130] Add 1 mL of detergent, incubate at 37°C for 2 minutes, and then centrifuge at 900g for 6 minutes to remove impurities.

[0131] Add 1 mL of culture broth and 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.

[0132] In order to effectively dissolve the cell membrane without causing excessive damage to the sample, the above-mentioned red blood cell lysis buffer and detergent need to be diluted in culture broth to a concentration of 1% in advance.

[0133] For positive blood culture samples, the pretreatment steps are relatively simple, as the pathogens have already multiplied significantly and impurities such as blood cells have been largely removed or degraded during the culture process. The main objectives are to remove most of the blood cells (if any remain) and dilute the sample to control the pathogen concentration, making it suitable for rapid antimicrobial susceptibility testing. Specifically, the following steps are involved:

[0134] 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.

[0135] The upper part of the clear liquid after centrifugation was taken as the bacterial sample without further lysis treatment.

[0136] If the bacterial concentration in the bacterial sample is high, it is diluted in gradients using culture broth (such as CAMHB culture broth), and the suspension that meets the preset bacterial concentration range is selected as the drug sensitivity sample using a large-field scattering imaging system.

[0137] The CAMHB culture broth must first be filtered through a 0.22 μm pinhole filter to remove any impurities. This step ensures the purity of the culture medium (CaMHB broth) used in the experiment. These impurities could interfere with subsequent scattering imaging, hindering accurate tracking of bacterial growth and determining drug susceptibility. Using a 0.22 μm pinhole filter effectively removes large particles and microbial contaminants from the culture medium.

[0138] In the embodiment of the present application, the initial particle concentration is set to be less than 2×10 5 This step is to adjust the bacterial concentration in the pretreated sample to a range suitable for antimicrobial susceptibility testing. By performing a concentration gradient dilution and counting using a wide-field scattering microscopy system, we ensure that the bacterial population in the antimicrobial susceptibility sample is moderate—neither too dense nor too sparse—facilitating subsequent growth tracking and antimicrobial susceptibility analysis.

[0139] 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.

[0140] The third group was set as the control group, i.e., drug-susceptibility samples without antibiotics, and added to the sixth cuvette.

[0141] The above-mentioned sensitive and intermediate breakpoint concentrations are pre-set. Breakpoint information for the target antibiotic is determined based on the Clinical and Laboratory Standards Institute (CLSI) antibiotic breakpoint information. Bacterial susceptibility to a particular antibiotic is defined by breakpoints and is categorized as sensitive (S), intermediate (I), and resistant (R). Susceptible refers to isolates with a minimum inhibitory concentration (MIC) equal to or lower than the sensitive breakpoint. At the recommended dose for treating the infection site, these isolates are inhibited by the commonly achievable antimicrobial concentration, resulting in a potential clinical response. Intermediate refers to isolates with an MIC in the middle range of the commonly achievable range, or isolates with a potential lower response rate than sensitive isolates. Resistant refers to isolates with an MIC higher than the resistance breakpoint and cannot be inhibited by the commonly achievable concentration of the drug under normal dosing conditions.

[0142] Antibiotic susceptibility results are determined according to the Clinical Laboratory Standards Institute (CLSI) susceptibility classification rules. Susceptibility test results are categorized as susceptible, intermediate, and resistant by measuring and comparing growth / inhibition curves at two breakpoint concentrations (a low concentration is the sensitive breakpoint and a high concentration is the intermediate breakpoint).

[0143] Place all cuvettes in the third set sequentially into the cuvette holder and prepare for image acquisition. Set the camera exposure time to 3600 μs and the frame rate to 5 fps, capturing 5 seconds of continuous data. This data is used as the bacterial image data at time 0 (i.e., the initial moment). An electronically controlled switching module can automatically switch the cuvette positions and record image data from each cuvette in the third set sequentially, saving time and improving detection efficiency.

[0144] To observe bacterial growth at different time points, image data from all three cuvettes must be recorded at regular intervals (e.g., 20 minutes). Single-bacteria scattering imaging and identification and counting techniques allow precise identification and counting of bacteria within the image, tracking bacterial growth in real time. This step is a core component of antimicrobial susceptibility testing, providing information on bacterial growth dynamics at varying antibiotic concentrations.

[0145] like Figure 2 As shown, Figure 2 This is a flow chart for processing the original single bacteria dynamic light scattering image provided in Example 2 of this 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 bacteria 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, forming an original single-bacteria dynamic light scattering image sequence.

[0146] The large-field scattering microscopy 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 with 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 should be no less than 10 CFU / mL.

[0147] During dynamic light scattering imaging, vibration noise may be introduced into the image sequence due to equipment or environmental instability, which can affect the accuracy of subsequent bacterial identification. The time-domain difference method compares the image differences between adjacent frames to identify and remove areas with large intensity variations caused by vibration. Specifically, it calculates the difference between two adjacent frames and removes areas in the difference image where the intensity exceeds a certain threshold, effectively eliminating vibration noise.

[0148] 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, the differential image is obtained by differentiating the 10th and 11th frames of images, and the intensity in the differential image exceeds the intensity threshold, then the 10th and 11th frames of original single bacteria dynamic light scattering images are deleted to eliminate the influence of vibration noise, and the first original image sequence is obtained. Figure 2 As shown in B (the sequence is not shown, only one of the pictures in the sequence is used as an example).

[0149] By calculating the temporal local minimum 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 2 As shown in C (the sequence is not shown, only one of the pictures in the sequence is used as an example).

[0150] Static noise typically refers to parts of an image that remain stationary or change very little, while drift noise is caused by slowly moving or unstable elements. 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).

[0151] Setting a smaller stack size ensures that static and slowly drifting noise is removed while retaining the signal from faster-moving bacteria, thus avoiding signal loss. Increasing the stack size, while it includes information from more frames, also increases the risk of including other dynamic changes unrelated to the current pixel. These dynamic changes may include the passage of other moving objects, changes in lighting conditions, camera shake, and other factors, all of which can interfere with the calculation of the local minimum in the temporal domain.

[0152] A stack refers to the range of frames used when calculating the local temporal minimum. If the stack is too large, so that it includes dynamic changes unrelated to the current pixel, then the calculated local temporal minimum may no longer simply reflect the level of background noise, but may be "contaminated" by these dynamic changes. In this case, the local temporal minimum may be overestimated because it contains additional, unnecessary intensity changes. When this "contaminated" local temporal minimum image 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 temporal minimum is overestimated, the removed noise may also contain the intensity of the bacterial signal.

[0153] The spatial local background of each pixel in the second original image sequence is calculated by averaging a large area within a preset radius, 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.

[0154] Spatial noise refers to randomly distributed intensity variations in an image that 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 that pixel can be calculated and subtracted from the second original image sequence to remove spatial background noise. The radius should be set to ensure that the largest target object is covered to avoid mistakenly removing bacterial signal as background.

[0155] 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).

[0156] The aforementioned image processing algorithm can employ a watershed algorithm, and the filter can employ the Laplacian of Gaussian operator. The watershed algorithm is a segmentation method based on image morphology. It simulates the concept of watersheds in topography and is used to divide continuous areas in an image into distinct sections. Here, it is combined with the Laplacian of Gaussian operator to accurately identify individual bacteria of varying morphologies. The Laplacian of Gaussian operator highlights edge information in an image, while the watershed algorithm uses this edge information to separate bacteria from the background.

[0157] 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 number. The sum of the number 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 the following example: Figure 2 As shown in F.

[0158] After identifying individual bacteria, the detected scattered light spots need to be further filtered to remove outliers (such as noise and artifacts). This can be achieved by applying filters such as minimum, signal-to-noise ratio, and standard deviation. Finally, each filtered scattered light spot image is counted, the sum is calculated, and then divided by the number of images to determine the total number of individual bacteria in the field of view.

[0159] 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.

[0160] 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.

[0161] The effective growth rate was calculated based on a growth inhibition model.

[0162] The model for changes in bacterial populations in the presence of commonly used antibiotics is as follows:

[0163]

[0164] 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.

[0165] For simplicity, the concentration of antibiotics is assumed to remain constant and effective during the time of rapid testing (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 is used as an impurity term for correction:

[0166] ;

[0167] In the initial stage, bacteria may not be in the logarithmic growth phase, so the delay in the effectiveness of antibiotics must be considered. In order to compensate for these delays, the above model was 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:

[0168] ;

[0169] Through the above model, we can calculate and fit t The number of bacterial cells in the system at a given moment:

[0170] ;

[0171] Effective growth rate K growth Can be used to quantify effective bacterial growth:

[0172] 。

[0173] After 120 minutes of continuous image data recording, a growth / killing time curve can be plotted based on the single bacterial cell count results. These curves are then analyzed and compared using the aforementioned growth inhibition analysis model to determine the effective growth rate ratio of the test group to the control group. This is then compared to the inhibition threshold to determine the bacterial sensitivity to the target antibiotic. Based on their growth at different antibiotic concentrations, bacteria can be classified as sensitive, intermediate, or resistant, providing important reference information for clinical treatment.

[0174] like Figure 3 As shown, Figure 3 This is a 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 B, TGC is polymyxin B, 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.

[0175] Table 1 Results of susceptibility testing of 7 antibiotics

[0176]

[0177] In the table, LVSi-AST represents the test results obtained using the method provided in the examples of this application. In these examples, the inhibition threshold is set at 0.09. Taking AMK as an example, if the effective growth rate ratio of bacteria at two concentrations is less than 0.09, bacterial growth is inhibited, and the AST (Antimicrobial Susceptibility Testing) result is S. CIP: If the effective growth rate ratio at the sensitive concentration is greater than 0.09, the bacteria are not inhibited, while if the effective growth rate ratio at the intermediate concentration is less than 0.09, bacterial growth is inhibited, the AST result is I. CX: If the effective growth rate at two concentrations is greater than 0.09, bacterial growth is not inhibited, the AST result is R. The susceptibility test results are consistent with those of the commonly used commercial instrument VITEK 2 in clinical practice and the gold standard method E-test, which can obtain MIC results.

[0178] 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.

[0179] A total of 200 tests were performed on 16 clinically positive blood culture samples at two breakpoint concentrations for multiple antibiotics. With an inhibition threshold of 0.09, 159 tests were classified as inhibition and 41 tests as growth within 120 minutes, with a concordance rate (concordant samples / total samples, compared with clinical VITEK 2 results) of 98%.

[0180] like Figure 5 As shown, Figure 5 This is a schematic diagram of a receiver operating characteristic curve provided in Example 1 of this 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.

[0181] like Figure 6 As shown, Figure 6 This is a comparison chart of the clinical VITEK 2 results provided in Example 1 of this application. 100 AST results were compared with the clinical VITEK 2 results, and the classification consistency rate reached 96%, with no major errors or very major errors.

[0182] In the examples of this application, we used two breakpoint concentrations to improve the accuracy of drug susceptibility testing, 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 generally closely related to the intermediate breakpoint concentration, which makes them highly sensitive to small changes in the accuracy of the prepared antibiotic concentration. 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.

[0183] The embodiment of the present application provides a method for rapid drug sensitivity detection of biological samples containing low-abundance bacteria. Through 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, significantly shortening the detection time from the traditional 2-5 days to a few hours, providing the possibility for rapid clinical decision-making; using single-bacteria dynamic scattering imaging technology, it can accurately identify and count individual bacteria, monitor bacterial growth in real time, and improve the sensitivity and accuracy of detection; combining 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 drug resistance problems caused by blind use of antibiotics, and improve treatment effects and patient survival rates.

[0184] Example 3:

[0185] 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 this application. The device includes:

[0186] 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;

[0187] a grouping module for dividing the biological samples into three groups and adding them into containers containing culture medium, wherein the first group is added with the target antibiotic at a sensitive breakpoint concentration to serve as a sensitive test group, the second group is added with the target antibiotic at an intermediate breakpoint concentration to serve as an intermediate test group, and the third group is not added with the antibiotic to serve as a control group;

[0188] a counting module, configured to obtain image data from all third-group cuvettes using single-bacteria scattering imaging at preset intervals to count the 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;

[0189] a calculation module, configured to calculate 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 determine the growth inhibition state of the bacteria in combination with the inhibition threshold;

[0190] 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.

[0191] Specifically, the counting module includes:

[0192] a differential calculation unit, configured to perform single bacterial scattering imaging on all cuvettes of the third group at preset time intervals, record multiple original single bacterial dynamic light scattering image sequences, and perform differential calculation on adjacent frames to obtain differential images, wherein each pixel value in the differential image represents an intensity change of a pixel at a corresponding position in the original single bacterial dynamic light scattering images of two adjacent frames; if the intensity in the differential image exceeds an intensity threshold, 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;

[0193] a temporal local minimum value calculation unit, configured to construct a background image by calculating the temporal 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;

[0194] 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 no less than the radius of the largest target detection object;

[0195] an identification unit, configured to identify individual bacteria of various morphologies in the third original image sequence using an image processing algorithm and a filter, and obtain a single bacteria scattered light spot image sequence;

[0196] 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.

[0197] 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 content in the aforementioned method Example 2.

[0198] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this application can be achieved. This is not a limitation herein.

[0199] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0200] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for detecting the bacterial growth status 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 first group is added with the target antibiotic and serves as the test group, while the second group is not added with the antibiotic and serves as the control group. At each preset time duration, single bacteria scattering imaging is used to obtain image data in all first group of cuvettes 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, calculating the ratio of the effective growth rate of the first cuvette to the effective growth rate of the second cuvette, and determining the growth inhibition state of the bacteria in combination with the inhibition threshold; The effective growth rate is calculated based on a growth inhibition model, which is: ； Where, 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 correction parameter; Based on the growth inhibition model, the number of bacterial cells at time t is calculated as: ； The effective growth rate is: .

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 to containers containing culture medium, respectively, wherein the first group comprises a plurality of said containers, and different concentrations of target antibiotics are added to different said containers, serving as the test group, and the second group does not contain antibiotics, serving as the control group; At predetermined intervals, single bacteria scattering imaging is used to obtain image data from all second group of cuvettes to count the 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, calculating the ratio of the effective growth rate of the third cuvette to the effective growth rate of the fourth cuvette, and determining the growth inhibition state of the bacteria in combination with the inhibition threshold; From the target antibiotic concentrations corresponding to the test group in which the bacteria are in an inhibited state, the minimum value is selected as the minimum inhibitory concentration of the target antibiotic; The effective growth rate is calculated based on a growth inhibition model, which is: ； Where, 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 correction parameter; Based on the growth inhibition model, the number of bacterial cells at time t is calculated as: ； The effective growth rate is: .

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 to containers containing culture medium respectively. The target antibiotic is added to the first group until the final antibiotic concentration is the sensitive breakpoint concentration, which serves as a sensitive test group. The target antibiotic is added to the second group until the final antibiotic concentration is the intermediate breakpoint concentration, which serves as an intermediate test group. No antibiotic is added to the third group, which serves as a control group. At predetermined intervals, single bacteria scatter imaging is used to obtain image data from all cuvettes in the third group, and the bacteria are counted, 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; When the predetermined recording time is reached, calculating the ratio of the effective growth rate of the fifth cuvette to the effective growth rate of the sixth cuvette, and determining the growth inhibition state of the bacteria in combination with the inhibition threshold; Determining the bacterial resistance to the target antibiotic based on the bacterial growth conditions of the sensitive test group and the intermediate test group; The effective growth rate is calculated based on a growth inhibition model, which is: ； Where, 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 correction parameter; Based on the growth inhibition model, the number of bacterial cells at time t is calculated as: ； The effective growth rate is: .

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 of all the third group of cuvettes by using single bacteria scattering imaging at preset time intervals and counting the bacteria includes: At predetermined intervals, single bacterial scattering imaging is performed on all cuvettes of the third group, and a sequence of original single bacterial dynamic light scattering images is recorded. Adjacent frame images are differentiated to obtain differential images, 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 bacterial dynamic light scattering images; if the intensity in the differential image exceeds an intensity threshold, images related to the differential image are removed from the sequence of original single bacterial dynamic light scattering images to obtain a first sequence of original images; A background image is constructed by calculating a temporal 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; Calculating the spatial local background of each pixel by averaging a large area within a preset radius around each pixel in the second original image sequence, and subtracting 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; using an image processing algorithm and a filter to identify individual bacteria of various morphologies 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, alveolar lavage fluid samples, pleural effusion samples, ascites samples, cerebrospinal fluid samples, serum samples, plasma samples, blood samples, urine samples, genital 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 step of calculating the ratio of the effective growth rate of the fifth cuvette to the effective growth rate 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.

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 determining the bacterial resistance to the target antibiotic based on the bacterial growth conditions of the sensitive test group and the intermediate test group includes: Determining 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.

9. A rapid drug sensitivity detection device for biological samples containing low-abundance bacteria, characterized in that: include: The acquisition module is used to obtain 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, configured to divide the biological samples into three groups, add the biological samples into containers containing culture medium respectively, add the target antibiotic to the first group until the final antibiotic concentration is the sensitive breakpoint concentration, and serve as the sensitive test group; add the target antibiotic to the second group until the final antibiotic concentration is the intermediate breakpoint concentration, and serve as the intermediate test group; and do not add the antibiotic to the third group, and serve as the control group; a counting module, configured to obtain image data from all third group of cuvettes using 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 contains a sensitive test group and an intermediate test group, and the sixth cuvette contains a control group; a calculation module, configured to calculate a ratio of the effective growth rate of the fifth cuvette to that of the sixth cuvette when a predetermined recording time is reached, and determine a growth inhibition state of the bacteria in combination with an inhibition threshold; a determination module, configured to determine the drug resistance of bacteria to a target antibiotic based on the bacterial growth conditions of the sensitive test group and the intermediate test group; The effective growth rate is calculated based on a growth inhibition model, which is: ； Where, 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 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 the effective growth rate 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 bacterial resistance to the target antibiotic based on the bacterial growth conditions of the sensitive test group and the intermediate test group includes: Determining 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. The rapid drug sensitivity detection device for biological samples containing low-abundance bacteria according to claim 9, characterized in that: The counting module includes: a differential calculation unit, configured to perform single bacterial scattering imaging on all cuvettes of the third group at preset time intervals, record a plurality of original single bacterial 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 an intensity change of a pixel at a corresponding position in two adjacent frames of the original single bacterial dynamic light scattering images; and if the intensity in the differential image exceeds an intensity threshold, 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 temporal local minimum value calculation unit, configured to construct a background image by calculating the temporal 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 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, configured to identify individual bacteria of various morphologies in the third original image sequence using an image processing algorithm and a filter, and obtain 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 images in the single bacteria scattered light spot image sequence to obtain the number of single bacteria.

11. The rapid drug sensitivity detection device for biological samples containing low-abundance bacteria according to claim 9, characterized in that: The biological samples containing low-abundance bacteria 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, genital secretion samples, and fecal samples.

Images