Culture detection method and system

Abstract

The invention discloses a culture detection method and system, and belongs to the field of biological detection.The method comprises the steps that a sample is divided into a plurality of micro-samples in a micro-fluidic chip, and the micro-samples are cultured to form a plurality of cultures; continuously collecting images of each culture to form an image set changing along with time; extracting the size, shape, density and position characteristics of the culture in each image, and performing time sequence analysis on the plurality of images in the image set; obtaining dynamic characteristics of each culture according to the extracted characteristics and the time sequence analysis result, wherein the dynamic characteristics comprise growth rate, morphological change, activity, movement speed and movement track; according to the method, the dynamic characteristics of all cultures are counted, the average value, the confidence interval and the standard deviation of each dynamic characteristic are calculated, at least one detection result of sample types, drug resistance and quantity is obtained according to the average value, the confidence interval and the standard deviation of the multiple dynamic characteristics, the detection result is fast, and therefore the problem that in the prior art, the bacterial detection speed is low is solved.

Description

Technical Field

[0001] The present invention relates to the field of biological detection, and in particular to a culture detection method and system. Background Art

[0002] Bacterial infection is an infection caused by pathogenic bacteria invading the human body. It can occur anywhere in the body, such as the skin surface, respiratory tract, digestive tract, urogenital tract, blood and meninges. With the continuous emergence and spread of antibiotic-resistant bacteria, rapid and reliable identification of infection has become the key to the correct treatment of bacterial infections. Once the bacteria are detected, it is necessary to determine its species and its antibiotic resistance profile to ensure the use of effective antibiotics and reduce dependence on broad-spectrum drugs, which is crucial for timely diagnosis and treatment.

[0003] At present, the gold standard method for bacterial resistance commonly used in clinical practice is the culture method. This method places the patient's sample in a culture medium containing nutrients, uses the nutrients in the culture medium to promote bacterial growth, and finally observes whether bacterial colonies appear in the culture dish to determine the type and number of infected bacteria. For positive samples determined to be bacterial infections, further sensitivity tests for different antibiotics are required to determine the best therapeutic drug. The culture method is highly accurate and reliable, and can clearly identify the type and number of bacteria, which helps doctors develop targeted treatment plans.

[0004] However, due to the time-consuming detection process of the culture method, sample culture usually takes 24 to 48 hours or even longer to obtain results, which may delay the timing of treatment and is not suitable for emergency or frequent testing. Therefore, although the culture method as the gold standard method has high accuracy and reliability, in actual application, it is still necessary to weigh its time cost against the timeliness of treatment guidance. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, one of the objectives of the present invention is to provide a culture detection method with a fast detection speed.

[0006] In order to overcome the deficiencies of the prior art, a second object of the present invention is to provide a culture detection system with a fast detection speed.

[0007] One of the purposes of the present invention is achieved by the following technical solution:

[0008] A culture detection method comprises the following steps:

[0009] loading a sample into a microfluidic chip, wherein the sample is divided into a plurality of microsamples in the microfluidic chip;

[0010] Each of the micro-samples is cultured in the microfluidic chip to form a culture;

[0011] continuously acquiring images of each of the cultures to form a collection of images of each of the cultures over time;

[0012] Identify the size, shape, density, and position features of the culture in each of the images in the image set, extract the size, shape, density, and position features of multiple images in the image set, and perform time series analysis on the multiple images in the image set;

[0013] Obtaining dynamic characteristics of each of the cultures according to the extracted characteristics and the time series analysis results, wherein the dynamic characteristics include growth rate, morphological change, activity, movement speed, and movement trajectory;

[0014] The dynamic characteristics of all cultures are counted, the average value, confidence interval, and standard deviation of each dynamic characteristic are calculated, and at least one detection result of sample type, drug resistance, and quantity is obtained based on the average values, confidence intervals, and standard deviations of multiple dynamic characteristics.

[0015] Furthermore, when the test result is drug resistance, the sample includes a sample containing antibiotics and a sample not containing antibiotics. When the upper limit of the confidence interval of the growth rate of the sample containing antibiotics is higher than the lower limit of the confidence interval of the growth rate of the sample not containing antibiotics, the drug resistance result is that the sample is sensitive to the antibiotic.

[0016] Furthermore, when the test result is drug resistance, an upper confidence limit curve of the growth rate of samples containing antibiotics and a lower confidence limit curve of the growth rate of samples not containing antibiotics are drawn, and the time when the two intersect is the shortest detection time.

[0017] Furthermore, when the detection result is drug resistance, the microfluidic chip is provided with at least two sample chambers, and a sample containing antibiotics and a sample not containing antibiotics are added to the two sample chambers respectively.

[0018] Furthermore, the growth rate is calculated using any one of the culture quantity, culture density and culture pixel ratio.

[0019] Furthermore, when the growth rate is calculated using the culture pixel ratio, the number of effective image pixels occupied by the culture corresponding to the i-th culture at the j-th time node is p ij , the number of effective pixels of the image is p i0 , the growth rate of the i-th culture at the j-th time point is

[0020] Furthermore, the sample is divided into multiple microsamples in the microfluidic chip specifically by: the sample forms any one of multiple independent droplets in the microfluidic chip, multiple micropores of the sample dispersion microfluidic chip, multiple microcavities of the sample dispersion microfluidic chip, and multiple microchannels of the sample dispersion microfluidic chip.

[0021] Furthermore, the step of continuously collecting images of each of the cultures specifically includes: using visible light imaging or fluorescence imaging to perform time-lapse in situ imaging monitoring of the area where each culture is located.

[0022] Furthermore, in the step of identifying the size, shape, density, and position features of the culture in each image in the image collection, a deep neural network model is used for identification. The deep neural network model includes image feature extraction and time series analysis. The image feature extraction uses a convolutional neural network architecture to encode high-dimensional images into low-dimensional features while retaining the spatial attributes of the image features. The time series analysis uses a recurrent neural network.

[0023] The second object of the present invention is achieved by adopting the following technical solution:

[0024] A culture detection system for implementing any one of the culture detection methods described above, the culture detection system comprising

[0025] A microfluidic chip, wherein the microfluidic chip is used to disperse a sample into a plurality of microsamples, and the plurality of microsamples are cultured in the microfluidic chip to form a culture;

[0026] A culture module, wherein the culture module controls the temperature in the microfluidic chip to provide an environmental temperature suitable for the culture;

[0027] A monitoring module, wherein the monitoring module collects images of each area where the culture is located that change over time;

[0028] A data processing module is used to analyze the images collected by the monitoring module to obtain at least one detection result of the sample type, drug resistance and quantity.

[0029] Compared with the prior art, the culture detection method of the present invention divides the sample into multiple microsamples in a microfluidic chip; each of the microsamples is cultured in the microfluidic chip to form a culture; the image of each of the cultures is continuously collected to form an image set of each of the cultures changing over time; the size, shape, density, and position features of the culture in each of the images in the image set are identified, the size, shape, density, and position features of multiple images in the image set are extracted, and time series analysis is performed on the multiple images in the image set; the dynamic features of each of the cultures are obtained based on the extracted features and the time series analysis results, and the dynamic features include growth rate, morphological changes, activity, movement speed, and movement trajectory; the dynamic features of all the cultures are counted, the average value, confidence interval, and standard deviation of each of the dynamic features are calculated, and at least one detection result of the sample type, drug resistance, and quantity is obtained based on the average value, confidence interval, and standard deviation of the multiple dynamic features, and the detection result is fast, thereby solving the problem of slow speed of the bacteria detection method in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flow chart of the culture detection method of the present invention;

[0031] Figure 2 is a schematic diagram of a culture detection system of the present invention;

[0032] Figure 3 is a schematic diagram of a microfluidic chip of the present invention;

[0033] Figure 4 This is a schematic diagram of deep neural network model recognition in the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0036] like Figure 1 As shown, the present invention provides a culture detection method, comprising the following steps:

[0037] The sample is loaded into a microfluidic chip, and the sample is divided into multiple microsamples in the microfluidic chip;

[0038] Each microsample is cultured in a microfluidic chip to form a culture;

[0039] continuously acquiring images of each culture to form a collection of images of each culture over time;

[0040] Identify the size, shape, density, and position features of the culture in each image in the image set, extract the size, shape, density, and position features of multiple images in the image set, and perform time series analysis on multiple images in the image set;

[0041] The dynamic characteristics of each culture were obtained based on the extracted features and time series analysis results. The dynamic characteristics included growth rate, morphological changes, activity, movement speed, and movement trajectory.

[0042] The dynamic characteristics of all cultures are counted, the average value, confidence interval, and standard deviation of each dynamic characteristic are calculated, and at least one test result of sample type, drug resistance, and quantity is obtained based on the average values, confidence intervals, and standard deviations of multiple dynamic characteristics.

[0043] Before the sample is loaded onto the microfluidic chip, it needs to be pre-treated to filter out unexpected particles. A filter area that removes interferences in the sample can also be set on the microfluidic chip to filter out unexpected particles.

[0044] The sample can be divided into multiple microsamples in the microfluidic chip by any of the following methods:

[0045] 1. The sample forms multiple independent droplets in the microfluidic chip;

[0046] 2. Multiple micropores of sample dispersion microfluidic chip;

[0047] 3. Multiple microcavities of sample dispersion microfluidic chip;

[0048] 4. The sample is dispersed in multiple microchannels of the microfluidic chip.

[0049] Among them, droplets, micropores, microcavities and microchannels can all be used as microchambers. The microchamber can accommodate single cells. The microchamber contains at least one transparent optical surface for imaging observation, which is convenient for image acquisition.

[0050] When a method is used to form multiple micro samples by forming multiple independent droplets in a microfluidic chip, specifically: the flow focusing method is used to prepare droplets as culture micro chambers: the aqueous sample containing the culture is used as the discrete phase, and the mineral oil that is immiscible with it is used as the continuous phase. Based on the liquid-liquid two-phase flow technology, the sample is sheared into independent reaction units, i.e. droplets, under the action of the oil phase, based on the liquid-liquid two-phase flow technology. The droplets are sheared into independent reaction units, i.e. droplets, and the droplet volume is between pL and nL. The generated droplets enter the droplet collection structure through the transport flow channel. The height of the droplet collection structure chamber is consistent with the droplet diameter. Under its restriction, the droplets are flattened into a single layer and can be used for detection. At least one side of the chip monitoring area is optically transparent for easy observation. In order to facilitate comparative studies, there are usually at least two groups of similar structures on the same chip.

[0051] When performing drug sensitivity testing, that is, when the test result is drug resistance, the samples include samples containing antibiotics and samples without antibiotics. The structure of the microfluidic chip is as follows Figure 3 As shown, the microfluidic chip is provided with two sample chambers, and the two sample chambers are respectively filled with a sample containing antibiotics and a sample without antibiotics.

[0052] In the step of culturing each micro sample in the microfluidic chip to form a culture, the culture module provides an external environment suitable for the culture by temperature control and other methods.

[0053] The images of each culture are continuously collected to form a set of images of each culture changing over time. Specifically, visible light imaging or fluorescence imaging is used to perform time-lapse in situ imaging monitoring of the area where each culture is located.

[0054] The size, shape, density, and position characteristics of the culture in each image in the image set are specifically:

[0055] In order to accurately identify the microchambers and cultures in the microscopic images, traditional machine vision or deep learning methods are used for processing. In this example, the deep learning method is used. Please continue to refer to Figure 4 ,The deep neural network model architecture is divided into two stages: image feature extraction and time series analysis. For the image feature extraction stage, convolutional neural network architectures such as ResNet-50 can be used to encode high-dimensional images into low-dimensional features while preserving their spatial properties. For the time series analysis stage, recurrent neural network architectures such as LSTM can be used.

[0056] The dynamic characteristics of each culture are obtained based on the extracted features and time series analysis results. The dynamic characteristics include growth rate, morphological changes, activity, movement speed, and movement trajectory. Specifically, after continuous shooting and extraction of the above features, machine learning (such as SVM) or deep learning methods can be used to track the movement and division of the culture, thereby confirming the dynamic characteristics such as the movement speed and trajectory of the culture in the microchamber, and then statistically analyzing the characteristics such as the type, density, activity, morphological changes and growth rate curve of the culture in each microchamber to characterize the growth status of the culture.

[0057] The growth rate is calculated by any one of the culture number, culture density and culture pixel ratio. When the growth rate is calculated by the culture pixel ratio, the number of effective pixels p of the image occupied by the culture corresponding to the i-th culture at the j-th time node is ij , the number of effective pixels of the image is p i0 , the growth rate of the i-th culture at the j-th time point is

[0058] The dynamic characteristics of all cultures are counted, and the average value, confidence interval, and standard deviation of each dynamic characteristic are calculated. According to the average values, confidence intervals, and standard deviations of multiple dynamic characteristics, at least one test result of sample type, drug resistance, and quantity is obtained, specifically:

[0059] The statistical features of all micro-samples, such as the mean value, upper and lower limits of the confidence interval, are counted to make an overall assessment of the growth status of the culture. The network of the deep neural network model architecture outputs the prediction results through a fully connected layer module. The model output is used for the prediction of growth rate estimation over time and the culture category label. In the model output, the samples are labeled according to the ratio of the starting concentration to the ending concentration and the culture category manually identified by domain experts. The network weights can be fine-tuned using the model weights pre-trained based on a general large data set and a relatively small custom data set. Based on the above network output results, a curve of the growth rate of the culture in a single microchamber over time can be drawn, and the growth rate of the culture in all microchambers containing cultures in the chip over time can be obtained to obtain the average value, standard deviation and 99% confidence interval of the growth rate curve.

[0060] When the upper limit of the confidence interval of the growth rate of the sample containing antibiotics is higher than the lower limit of the confidence interval of the growth rate of the sample without antibiotics, the drug resistance result is that the sample is sensitive to the antibiotic. When the test result is drug resistance, the upper confidence limit curve of the growth rate of the sample containing antibiotics and the lower confidence limit curve of the growth rate of the sample without antibiotics are drawn, and the time when the two intersect is the shortest detection time.

[0061] When the above culture detection method is used for Escherichia coli drug sensitivity detection, the specific steps are as follows:

[0062] (1) Take a sample and dilute the original bacterial solution to 1×105 CFU / mL using sterile liquid culture medium.

[0063] (2) Take a microfluidic chip for detection, add 10 μL of Escherichia coli solution to the sample input port of the in-situ microfluidic chip, and add culture medium containing antibiotic ciprofloxacin and culture medium without antibiotics to the culture medium input port to generate droplets.

[0064] After the droplets are generated, the microfluidic chip is placed on the microscope stage to image the detection chamber. The shooting parameters may vary depending on the growth rate. The imaging system magnification is 40X or 20X, the shooting interval is 5s-15min, and the total time is 30min-4h. According to performance requirements, the sample bacterial solution can also use other volumes such as (1-1000uL), and pre-treatment such as enrichment and filtration is performed before generating droplets.

[0065] Based on the continuously captured colony images, the number of effective pixels p of the image occupied by live bacteria in the i-th chamber at the j-th time node is counted. ij , and the number of effective pixels p of the image occupied i0 Calculate the bacterial growth rate of the i-th chamber at this moment Bacterial growth rate can also be characterized by other methods, such as the number of viable bacteria, density, etc.

[0066] Count the average growth rate of all effective chambers containing live bacteria at the jth time node Standard deviation S(L j ) and 99% confidence interval, Among them, P is the confidence interval, a j 、b j are the growth rates L at time j j The 99% confidence interval upper and lower limits.

[0067] The upper confidence limit curve of the growth rate of samples containing antibiotics and the lower confidence limit curve of the growth rate of samples not containing antibiotics were drawn respectively, and the time when the two intersect is the shortest detection time.

[0068] If at the end of the final detection time, the upper confidence limit of the growth rate of the sample containing antibiotics is still higher than the lower confidence limit of the growth rate of the sample without antibiotics, it proves that the two cannot be distinguished and the sample has a high resistance to antibiotics; otherwise, it can be considered that the sample is sensitive to antibiotics.

[0069] Please continue reading Figure 2 The present application also discloses a culture detection system for implementing the above culture detection method. The culture detection system includes

[0070] A microfluidic chip is used to disperse a sample into a plurality of microsamples, and the plurality of microsamples are cultured in the microfluidic chip to form a culture;

[0071] A culture module controls the temperature in the microfluidic chip to provide a suitable environment temperature for the culture;

[0072] A monitoring module, which collects images of each culture area changing over time;

[0073] The data processing module analyzes the images collected by the monitoring module to obtain at least one detection result of the sample type, drug resistance and quantity.

[0074] Specifically, the function of the microfluidic chip is to perform fine operations on samples, such as dispersing cells and other cultures in the sample into independent droplets, micropores, microcavities, microchannels and other microchambers that can accommodate single cells, and the microchambers contain at least one transparent optical surface for imaging observation. The microfluidic chip also contains a filtration area to remove interferences in the sample.

[0075] The monitoring module uses visible light imaging, fluorescence imaging and other detection methods to perform delayed in-situ imaging monitoring of each microchamber.

[0076] The culture module provides an external environment suitable for the culture through temperature control and other methods.

[0077] The data processing module analyzes the data acquired by the monitoring module and extracts useful information, such as cell number, growth rate, morphological changes, etc. to characterize the growth status of the culture.

[0078] The culture detection system also includes a fluid driving component, a fluid pipeline, an automatic control module and a human-computer interaction module.

[0079] The fluid drive module controls the flow of samples and reagents such as culture fluid, drugs, and cleaning fluid through various peristaltic pumps, syringe pumps, one-way valves, multi-way valves, etc., and disperses the culture into the microchamber through the fluid pipeline.

[0080] The automatic control module is used to manage the functions of the culture detection system, such as managing the operation of the fluid drive module, temperature control, scheduling the monitoring module for timed imaging and any other instruments related to the micro-nanofluidic chip.

[0081] The human-computer interaction module is responsible for the communication between the user and the system. Through a friendly graphical user interface (GUI), the user can intuitively control and monitor the operating status of the entire system.

[0082] The subsystem consisting of the automatic control module, the data processing module, and the human-computer interaction module can be integrated inside the instrument, or it can be controlled outside the instrument by a general-purpose computer, a special-purpose computer, a personal computer, a tablet device, an intelligent mobile device, a microprocessor or other programmable data processing device. In addition to the above functions, the subsystem can also provide processing capabilities, such as storing, interpreting and / or executing software instructions. The controller can be configured and programmed to control the data and / or power supply aspects of the culture monitoring system. The data storage device can be built into the subsystem or provided separately from the subsystem. The instrument can be connected to a network. For example, the communication interface of the controller can communicate with a networked computer. The networked computer can be any centralized server or a cloud-based server.

[0083] Compared with the prior art, the culture detection method of the present invention divides the sample into multiple microsamples in a microfluidic chip; each microsample is cultured in a microfluidic chip to form a culture; the image of each culture is continuously collected to form an image set of each culture changing over time; the size, shape, density, and position features of the culture in each image in the image set are identified, the size, shape, density, and position features of multiple images in the image set are extracted, and time series analysis is performed on the multiple images in the image set; the dynamic features of each culture are obtained based on the extracted features and the time series analysis results, and the dynamic features include growth rate, morphological changes, activity, movement speed, and movement trajectory; the dynamic features of all cultures are counted, the average value, confidence interval, and standard deviation of each dynamic feature are calculated, and at least one detection result of the sample type, drug resistance, and quantity is obtained based on the average value, confidence interval, and standard deviation of the multiple dynamic features, and the detection result is fast, thereby solving the problem of slow speed of the bacterial detection method in the prior art.

[0084] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which are equivalent modifications and improvements made to the above embodiments based on the essential technology of the present invention, and all of them belong to the protection scope of the present invention.

Claims

1. A culture detection method, characterized in that: The following steps are involved: loading a sample into a microfluidic chip, wherein the sample is divided into a plurality of microsamples in the microfluidic chip; Each of the micro-samples is cultured in the microfluidic chip to form a culture; continuously acquiring images of each of the cultures to form a collection of images of each of the cultures over time; Identify the size, shape, density, and position features of the culture in each of the images in the image set, extract the size, shape, density, and position features of multiple images in the image set, and perform time series analysis on the multiple images in the image set; Obtaining dynamic characteristics of each of the cultures according to the extracted characteristics and the time series analysis results, wherein the dynamic characteristics include growth rate, morphological change, activity, movement speed, and movement trajectory; The dynamic characteristics of all cultures are counted, the average value, confidence interval, and standard deviation of each dynamic characteristic are calculated, and at least one detection result of sample type, drug resistance, and quantity is obtained based on the average values, confidence intervals, and standard deviations of multiple dynamic characteristics.

2. The culture detection method according to claim 1, characterized in that: When the test result is drug resistance, the sample includes samples containing antibiotics and samples not containing antibiotics. When the upper limit of the confidence interval of the growth rate of the sample containing antibiotics is higher than the lower limit of the confidence interval of the growth rate of the sample not containing antibiotics, the drug resistance result is that the sample is sensitive to the antibiotic.

3. The culture detection method according to claim 2, characterized in that: When the test result is drug resistance, draw the upper confidence limit curve of the growth rate of samples containing antibiotics and the lower confidence limit curve of the growth rate of samples not containing antibiotics, and the time when the two intersect is the shortest detection time.

4. The culture detection method according to claim 2, characterized in that: When the detection result is drug resistance, the microfluidic chip is provided with at least two sample chambers, and a sample containing antibiotics and a sample not containing antibiotics are added to the two sample chambers respectively.

5. The culture detection method according to claim 1, characterized in that: The growth rate is calculated using any one of the culture number, culture density, and culture pixel ratio.

6. The culture detection method according to claim 5, characterized in that: When the growth rate is calculated using the culture pixel ratio, the number of effective image pixels occupied by the culture corresponding to the i-th culture at the j-th time node is p ij , the number of effective pixels of the image is p i0 , the growth rate of the i-th culture at the j-th time point is 7. The culture detection method according to claim 1, characterized in that: The sample is divided into multiple micro samples in the microfluidic chip specifically by: the sample forms any one of multiple independent droplets in the microfluidic chip, multiple micropores of the sample dispersion microfluidic chip, multiple microcavities of the sample dispersion microfluidic chip, and multiple microchannels of the sample dispersion microfluidic chip.

8. The culture detection method according to claim 1, characterized in that: The step of continuously collecting images of each of the cultures specifically comprises: using visible light imaging or fluorescence imaging to perform time-lapse in-situ imaging monitoring of the area where each culture is located.

9. The culture detection method according to claim 1, characterized in that: In the step of identifying the size, shape, density, and position features of the culture in each image in the image collection, a deep neural network model is used for recognition. The deep neural network model includes image feature extraction and time series analysis. The image feature extraction uses a convolutional neural network architecture to encode high-dimensional images into low-dimensional features while retaining the spatial attributes of the image features. The time series analysis uses a recurrent neural network.

10. A culture detection system for implementing the culture detection method according to any one of claims 1 to 9, characterized in that: The culture detection system comprises A microfluidic chip, wherein the microfluidic chip is used to disperse a sample into a plurality of microsamples, and the plurality of microsamples are cultured in the microfluidic chip to form a culture; A culture module, wherein the culture module controls the temperature in the microfluidic chip to provide an environmental temperature suitable for the culture; A monitoring module, wherein the monitoring module collects images of each area where the culture is located that change over time; A data processing module is used to analyze the images collected by the monitoring module to obtain at least one detection result of the sample type, drug resistance and quantity.

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