CN114414850B - Bacterial activity detection method, apparatus, device and storage medium thereof - Google Patents
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Abstract
The application discloses a bacterial activity detection method, a device, equipment and a storage medium thereof, wherein the method comprises the following steps: preparing a live bacterial liquid and a dead bacterial liquid; respectively obtaining a first force spectrum of living bacteria and a second force spectrum of dead bacteria through an atomic force microscope, and carrying out post-treatment on the obtained force curve to extract deformation, rigidity and Young modulus; the deformation, rigidity and Young's modulus are input into a machine learning model to judge the activity of bacteria. According to the scheme, the nano mechanical property of the bacteria can be used as the characteristic for judging the activity of the bacteria, so that the problem that dead bacteria still complete in cells cannot be detected is avoided, in addition, in the detection process, the probe of the atomic force microscope acts on the surface of the bacteria by using the force of the Pi-ox level, the force is far smaller than the mechanical external force which is lethal to the bacteria, so that the bacteria cannot be damaged in the test, in-situ detection can be carried out under the physiological condition, and the rapid and accurate analysis of machine learning is combined, so that the activity detection efficiency of the bacteria is greatly improved.
Description
Technical Field
The invention relates to the technical field of bacteria, in particular to a method, a device and equipment for detecting bacterial activity by combining an atomic force microscope force spectrum and an artificial intelligence technology and a storage medium thereof.
Background
The bacterial activity detection is widely applied to the industries of pharmacy, medical treatment, food and the like, and is an important part in the fields of drug sensitivity test, sterilization material development, food processing sterilization and the like. Colony counting based on plate culture is a classical method for evaluating bacterial activity by using whether bacteria can reproduce or grow as a criterion for judging whether bacteria live or dead, i.e., viable bacteria but dead bacteria cannot grow when cultured in a proper medium and temperature. The method comprises the following steps: the sample to be measured is diluted and coated on a flat plate, and after culture, each single cell grows and breeds to form macroscopic bacterial colony, namely, one single bacterial colony should represent one single cell in the original sample, then the bacterial colony number is counted, and the bacterial count in the sample can be calculated according to the dilution multiple and the sampling inoculation quantity. The staining method is the currently mainstream bacterial activity detection method, and the method uses membrane integrity as a judgment basis for bacterial activity. The commonly used dyes are SYTO 9 and Propidium Iodide (PI), in this double staining system, SYTO 9 (Green fluorescent nucleic acid dye) can penetrate into all bacterial membranes to make the living bacteria green fluoresce, while PI cannot penetrate into the whole cell membrane, can only enter into dead cells with damaged cell membranes to bind with DNA and release red fluorescence. Fluorescence measurement typically uses a microscope, fluorometer or flow cytometer. Ding Xianting et al propose a double enzyme amplification system and a method for detecting bacterial activity based on the same, which distinguish live bacteria from dead bacteria by detecting the sugar metabolism of bacteria. According to the method, glucose is added into the bacterial liquid to be detected, glucose is consumed by living bacteria through a glucose metabolic process, so that double-enzyme catalytic reaction is inhibited, the solution is colorless, glucose is not consumed by dead bacteria, and the two-step catalytic reaction is not affected, so that the solution is dark red. Sample bacterial activity was obtained by RGB analysis of the solution chromogenic results. Qiao Liang et al propose a method for detecting bacterial activity based on a colorimetric method by using a laser analysis ionization mass spectrometry technology as a detection means, and analyzing and detecting bacterial activity by detecting the change of molecular weight of an indicator before and after reduction by living bacteria and calculating the ratio of peak intensity of the indicator. Longo et al rapidly judged the activity of bacteria in antibiotic solutions by monitoring the vibration of the cantilever beam of the AFM probe. The method is based on the metabolic activity of bacteria to evaluate the activity of bacteria, and living bacteria produce larger cantilever fluctuation than dead bacteria exposed to antibiotics. Machine learning is also used for bacterial activity determination, and peguier et al acquire live and dead bacterial morphology pictures using a microscope and train a machine learning model to distinguish between dead and live bacteria based on morphological differences between live and dead bacteria. Yu et al compress the acquired bacterial video into a static picture containing single bacterial features, and determine the activity of the bacteria by using a deep learning algorithm according to the morphology and dynamic features of the bacteria.
However, culture-based methods have the major disadvantage of taking a long time (at least 24-48 hours for most clinical pathogens) and that viable bacteria that cannot reproduce cannot be detected, e.g. some bacteria in dormant state or damaged bacteria remain viable but are judged to be dead due to no dividing ability. In addition, it is difficult to determine a medium and culture conditions satisfying physiological needs of certain bacteria which are very critical to growth conditions or novel bacteria. Thus, the method of culturing easily underestimates the number of viable bacteria in the sample. The disadvantage of the staining method is that the dye is often toxic and prolonged exposure to the dye can affect the viability of the bacteria and even lead to death. In addition, this membrane integrity-based judgment method is not suitable for dead bacterial bundles with intact cell membranes, for example, bacteria can still maintain intact cell membranes after some inactivation treatments such as short wave ultraviolet rays, sunlight irradiation, antibiotics for inhibiting transcription replication of nucleic acids or protein synthesis, and PI cannot penetrate cells within hours or days after the bacteria are inactive, so that the number of dead bacteria can be underestimated. The detection method based on the double-enzyme system and the method based on AFM cantilever vibration can only qualitatively judge the activity of bacteria, and the accurate quantitative result is difficult to give, and the living and dead states of single bacteria cannot be obtained. In addition, by means of an AFM method, the existing commercial AFM needs to be subjected to modification test, which is tedious and time-consuming; in addition, the method can only detect death caused by a liquid environment, and has the limitation of single detection scene. The method for judging the bacterial activity by using the machine learning algorithm takes the difference of the shapes of living bacteria and dead bacteria as a criterion, on one hand, the dimension of input data such as video, images and the like is high, so that the calculated amount is huge, and the problem that the original data is difficult to avoid when the dimension reduction processing is carried out is solved; on the other hand, the judging mode based on the morphology is similar to that of taking membrane integrity as a criterion, and bacteria with incomplete morphology are regarded as dead bacteria, which also causes the problem of misjudging the bacteria with lost activity but complete morphology as living bacteria.
Disclosure of Invention
In view of the foregoing drawbacks or shortcomings of the prior art, it is desirable to provide a method, apparatus, device and storage medium for detecting bacterial activity that combines atomic force microscopy force spectrometry with artificial intelligence techniques.
In a first aspect, embodiments of the present application provide a method for detecting bacterial activity, the method comprising: preparing a live bacterial liquid and a dead bacterial liquid; respectively obtaining a first force spectrum of living bacteria and a second force spectrum of dead bacteria through an atomic force microscope, and carrying out post-treatment on force curves in the obtained first force spectrum and second force spectrum to extract deformation, rigidity and Young modulus; the deformation, rigidity and Young's modulus are input into a trained machine learning model to judge the activity of bacteria.
In one embodiment, the preparing of the live bacterial liquid and the dead bacterial liquid comprises: performing oxygen plasma activation on a sterile glass bottom culture dish, then dropwise adding 200 mu L of polylysine solution with the concentration of 0.01%, washing the surface with deionized water after five minutes, and then drying with nitrogen to obtain a polylysine modified glass substrate; 3mL of the bacterial liquid is placed in a centrifuge tube, centrifuged at 5000rpm for five minutes, the uniform bacterial liquid is dripped on a glass substrate modified by polylysine for fixation, and after five minutes, the bacterial liquid is respectively washed three times with 1mL of phosphate buffer salt solution, and the non-fixed bacteria are removed.
In one embodiment, the acquiring, by an atomic force microscope, the first force spectrum of the living bacteria and the second force spectrum of the dead bacteria, respectively, includes: the silicon nitride triangle cantilever probe on the atomic force microscope is vertically close to live bacteria and dead bacteria; the force exerted by the silicon nitride triangular cantilever probe on the bacteria is controlled to reach a set value, then the atomic force microscope controls the silicon nitride triangular cantilever probe to leave the living bacteria and the dead bacteria, and simultaneously, the atomic force microscope measures and records the force exerted by the probe in the process, so that a force curve is obtained.
In one embodiment, the post-processing the force curves in the obtained first force spectrum and the second force spectrum to extract deformation, stiffness and young's modulus includes: finding the contact point of the probe tip and the bacterial surface from the force curve, wherein the deformation is the distance between the contact point and the force set point, namely the distance of probe pressing in; obtaining stiffness from a slope fit in the force curve; young modulus was obtained by fitting the Sneddon model to the pressed-in segment of the force curve.
In one embodiment, the inputting the deformation, stiffness, young's modulus into the trained machine learning model to determine the activity of the bacteria comprises: an integrated learning algorithm is established by adopting a stacking method; constructing a machine learning model according to an integrated learning algorithm; the deformation, stiffness and Young's modulus are input into a constructed machine learning model to judge the activity of bacteria.
In a second aspect, embodiments of the present application also provide a bacterial activity detection device comprising: the preparation unit is used for preparing live bacterial liquid and dead bacterial liquid; an acquisition unit for acquiring a first force spectrum of the living bacteria and a second force spectrum of the dead bacteria respectively through an atomic force microscope, and performing post-treatment on force curves in the acquired first force spectrum and second force spectrum to extract deformation, rigidity and Young modulus; and the judging unit is used for inputting the deformation, the rigidity and the Young modulus into the trained machine learning model to judge the activity of the bacteria.
In one embodiment, the acquisition unit includes: the moving unit is used for vertically approaching the silicon nitride triangular cantilever probe on the atomic force microscope to living bacteria and dead bacteria; and the control unit is used for controlling the force exerted on the bacteria by the silicon nitride triangular cantilever probe to reach a set value, then controlling the silicon nitride triangular cantilever probe to leave the living bacteria and the dead bacteria by the atomic force microscope, and simultaneously measuring and recording the force exerted by the probe in the process by the atomic force microscope so as to obtain a force curve.
In one embodiment, the judging unit includes: the building unit is used for building an integrated learning algorithm by adopting a stacking method; the building unit is used for building a machine learning model according to the integrated learning algorithm; and the analysis unit is used for inputting the deformation, the rigidity and the Young modulus into the constructed machine learning model to judge the activity of the bacteria.
In a third aspect, embodiments of the present application further provide a computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing a method as described in any of the embodiments of the present application when the program is executed.
In a fourth aspect, embodiments of the present application further provide a computer apparatus, a computer readable storage medium having stored thereon a computer program for: the computer program, when executed by a processor, implements a method as described in any of the embodiments of the present application.
The invention has the beneficial effects that:
according to the method for detecting the bacterial activity, provided by the invention, the force spectrums of living bacteria and dead bacteria are respectively obtained through an atomic force microscope, then the obtained force curves are subjected to post-treatment to extract the deformation, rigidity and Young modulus, and the data are input into a machine learning model, so that the bacterial activity can be judged through the mechanical characteristic information of the bacterial surface. In addition, in the detection process, the probe of the atomic force microscope acts on the surface of the bacteria with a force of a Pi-ox grade, which is far smaller than the mechanical external force lethal to the bacteria, so that the bacteria cannot be damaged in the test, the in-situ detection can be carried out under the physiological condition, and the rapid and accurate analysis of the machine learning can be combined.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:
FIG. 1 shows a schematic flow chart of a method for detecting bacterial activity provided in an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for detecting bacterial activity according to another embodiment of the present application;
FIG. 3 shows an exemplary block diagram of a bacterial activity detection device 300 according to one embodiment of the present application;
FIG. 4 shows an exemplary block diagram of a bacterial activity detection device 400 according to a further embodiment of the present application;
FIG. 5 shows a schematic diagram of a computer system suitable for use in implementing the terminal device of the embodiments of the present application;
FIG. 6 shows a schematic diagram of a bacterial sample preparation process provided by an embodiment of the present application;
FIG. 7 shows a schematic diagram of an exemplary force profile of an AFM provided by an embodiment of the present application;
FIG. 8 is a schematic diagram showing exemplary force curves of AFM measurement of E.coli at different locations provided in the examples of the present application;
FIG. 9 is a schematic diagram of a machine learning process for detecting bacterial activity provided by embodiments of the present application;
fig. 10 shows a confusion matrix and ROC curves provided by embodiments of the present application.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The present invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the invention, whereby the invention is not limited to the specific embodiments disclosed below.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present invention, unless expressly stated or limited otherwise, a first feature "up" or "down" a second feature may be the first and second features in direct contact, or the first and second features in indirect contact via an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.
It will be understood that when an element is referred to as being "fixed" or "disposed" on another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and the like are used herein for illustrative purposes only and are not meant to be the only embodiment.
Referring to fig. 1, fig. 1 shows a flow chart of a bacterial activity detection method according to an embodiment of the present application.
As shown in fig. 1, the method includes:
step 110, preparing a live bacterial liquid and a dead bacterial liquid;
step 120, respectively obtaining a first force spectrum of living bacteria and a second force spectrum of dead bacteria by an atomic force microscope, and carrying out post-treatment on force curves in the obtained first force spectrum and second force spectrum to extract deformation, rigidity and Young modulus;
and 130, inputting the deformation, the rigidity and the Young modulus into a trained machine learning model to judge the activity of the bacteria.
The preparation of bacterial samples in the present application may be as shown in figure 6,
(1) Preparation of live bacterial liquid
Inoculating Escherichia coli 01 onto agar medium, incubating in a constant temperature incubator at 37deg.C for 10 hr until logarithmic phase, scraping appropriate amount of bacteria with sterile ring into phosphate buffer saline solution containing 3mL, blowing bottom of centrifuge tube with pipetting gun, centrifuging at 5000rpm for 5 min to obtain uniform viable bacteria suspension 02, diluting with phosphate buffer saline solution, and collecting viable bacteria suspension in 96-well plate until OD measured by enzyme marker 600nm A value of 0.15 gives a viable bacterial solution 05.
(2) Preparation of dead bacterial liquid
And obtaining a dead bacteria sample by adopting an ultraviolet sterilization mode. According to the preparation steps of (1) viable bacteria liquid to obtain uniform viable bacteria suspension, transferring viable bacteria liquid into a coverless culture dish 03 with a diameter of 35mm, and measuring the distance between the culture dish and an ultraviolet sterilizing lamp (wavelength: 254nm, power: 36W, energy density: 7 mJ/cm) 2 ) And irradiating for 20min at 20cm to obtain dead bacterial liquid 06.
By adopting the technical scheme, the force spectrums of the living bacteria and the dead bacteria are respectively obtained through an atomic force microscope, then the obtained force curves are subjected to post-treatment to extract the deformation, the rigidity and the Young modulus, and the data are input into a machine learning model, so that the activity of bacteria can be judged through the mechanical characteristic information of the surfaces of the bacteria. In addition, in the detection process, the probe of the atomic force microscope acts on the surface of the bacteria with a force of a Pi-ox grade, which is far smaller than the mechanical external force lethal to the bacteria, so that the bacteria cannot be damaged in the test, and the method can be combined with rapid and accurate analysis of machine learning, so that the bacterial activity can be judged within one hour, and the bacterial activity detection efficiency is greatly improved.
In some embodiments, to prevent bacterial drift during atomic force microscopy, polylysine is used to immobilize bacteria on a substrate, which has good biocompatibility, and the principle of immobilization of bacteria is electrostatic adsorption. The preparation of the live bacterial liquid and the dead bacterial liquid in the application comprises the following steps:
an aseptic glass bottom petri dish (diameter 35 mm) was subjected to oxygen plasma activation (120 w,2 min), then 200 μl of a 0.01% polylysine solution was added dropwise, and after five minutes the surface was rinsed with deionized water, followed by drying with nitrogen gas to obtain a polylysine-modified glass substrate. Before bacteria are immobilized, 3mL of bacterial liquid is placed in a centrifuge tube, centrifuged at 5000rpm for five minutes, the uniform bacterial liquid is dripped on a polylysine modified glass substrate for immobilization, and after five minutes, 1mL of phosphate buffer salt solution is respectively used for flushing three times, and the bacteria which are not immobilized are removed.
In some embodiments, the acquiring, by atomic force microscopy, the first force spectrum of the live bacterial fluid and the second force spectrum of the dead bacterial fluid, respectively, comprises: the silicon nitride triangle cantilever probe on the atomic force microscope is vertically close to live bacteria and dead bacteria; the force exerted by the silicon nitride triangular cantilever probe on the bacteria is controlled to reach a set value, then the atomic force microscope controls the silicon nitride triangular cantilever probe to leave the living bacteria and the dead bacteria, and simultaneously, the atomic force microscope measures and records the force exerted by the probe in the process, so that a force curve is obtained.
Specifically, the application fixes living bacteria and dead bacteria on a polylysine modified glass bottom culture dish according to the method. The petri dish was placed on a sample stage of an AFM (JPK NanoWizard 4XP, bruk, germany) integrated with an inverted optical microscope (Eclipse Ti2, japan nikon) with the whole set of system on an active vibration isolation platform. The test was performed in phosphate buffered saline to ensure that the bacteria remained physiologically active during the test using a silicon nitride triangle cantilever probe (model: scanasyst fluid, bruce Germany). Prior to each experiment, the probes were calibrated in phosphate buffered saline with a contact-based method to obtain sensitivity and spring constant (essentially between 0.7 and 1.3N/m). By using QI mode for testing, the topography map and force curve can be obtained at the same time, and the parameters are as follows: setpoint is 0.5nN, Z-length is 0.1 μm, and cantilever motion speed is 3 μm/s. The force applied to E.coli at this parameter was 0.5nN, which is much less than the force that would cause damage to E.coli (-6 nN). Each experiment time is not more than 3 hours, so as to avoid the change of bacterial surface properties possibly caused by factors such as temperature oscillation, nutrition deficiency and the like.
Further, the post-processing of the force curves in the obtained first force spectrum and the obtained second force spectrum extracts deformation, rigidity and Young modulus, and the post-processing comprises the following steps: finding the contact point of the probe tip and the bacterial surface from the force curve, wherein the deformation is the distance between the contact point and the force set point, namely the distance of probe pressing in; obtaining stiffness from a slope fit in the force curve; young modulus was obtained by fitting the Sneddon model to the pressed-in segment of the force curve.
Specifically, as shown in FIG. 7, it contains important information on the interaction between the sample and the tip. The AFM probe is brought vertically closer to the sample until the force reaches the setpoint, and then the cantilever is withdrawn away from the sample surface, during which the AFM measures and records the force the probe is subjected to, resulting in a force profile. From fig. 7 it can be seen that the contact point of the tip with the bacterial surface can be found from the indentation force curve, the amount of deformation being the distance between the contact point and the force set point, i.e. the distance the probe is indentation. Stiffness is obtained by slope fitting to the indentation curve. The Young's modulus is determined by fitting a Sneddon model to the pressed-in segment of the force curve, the Sneddon model relates the deformation d to the acting force F by the Young's modulus E, and the half angle alpha of the conical probe is considered:where v is poisson's ratio, typically 0.5 is chosen for biological materials and 19 ° is chosen for the probe α used in this experiment. The above data on deflection, stiffness and young model were all extracted and analyzed off-line by JPK data post-processing software.
The mechanical properties of the bacteria were obtained by extracting force curves from different locations of the bacteria, which were collected in three specific areas of the same bacteria (both ends and center of the bacteria), each area being randomly selected from 3 points of the recorded force curves (as shown in fig. 8 a-c, the star shape represents the selected area). In this example, 20 live bacteria and 20 dead bacteria were obtained for analysis in at least 5 independent experiments, respectively. Since the noise is inevitably generated in the force curve by the environmental interference, the force curve needs to be smoothed and denoised first in the post-processing, and a gaussian filter is selected to filter the noise, and the width of the filter is 8. The shape of the force curve after filtering remains unchanged, retaining the original information of the force curve, and typical force curves for the three regions are shown as d-f in fig. 8, respectively.
In some embodiments, referring to fig. 2, fig. 2 shows a schematic flow chart of a method for detecting bacterial activity according to another embodiment of the present application.
As shown in fig. 2, inputting the deformation, stiffness, young's modulus into a trained machine learning model to determine the activity of bacteria, comprising:
step 210, establishing an integrated learning algorithm by adopting a stacking method;
step 220, constructing a machine learning model according to an integrated learning algorithm;
in step 230, the deformation, stiffness and Young's modulus are input into the constructed machine learning model to judge the activity of the bacteria.
The integrated algorithm builds a plurality of machine learning models, and the learning task is completed through a certain strategy combination, so that the advantages and disadvantages of different base models can be combined, and a model with higher accuracy and better robustness can be obtained. The invention adopts a stacking method (stacking) to establish an integrated learning algorithm, and a base model comprises four commonly used classifiers: extreme gradient lifting (XGBoost), gradient lifting (GB), random Forest (RF), and adaptive lifting (AdaBoost), the meta-model is a Logistic Regression (LR) algorithm. The concept of Stacking is to learn several different weak learners and combine them by training a meta-model and then output the final prediction based on the multiple predictions returned by these weak models, thus achieving higher prediction accuracy. In this example, three characteristics of deformation, rigidity and Young's modulus are used as inputs of a model to predict the viability of bacteria, a model flow chart is shown in FIG. 9, and an analyzed data set comprises 356 force curves and two labels, namely live E.coli or dead E.coli, and the data is randomly divided into a training set and a test set (252 training samples and 104 test samples) in a ratio of 7:3. Classifier training and validation was performed in juyter Notebook (v6.0.0). The super parameter tuning method applies grid searching.
Further, referring to fig. 3, fig. 3 shows an exemplary block diagram of a bacterial activity detection device 300 according to one embodiment of the present application.
As shown in fig. 3, the apparatus includes:
a preparation unit 310 for preparing a live bacterial liquid and a dead bacterial liquid;
an obtaining unit 320, configured to obtain a first force spectrum of a living bacterium and a second force spectrum of a dead bacterium by using an atomic force microscope, and perform post-processing on force curves in the obtained first force spectrum and second force spectrum to extract deformation, rigidity, and young's modulus;
and a judging unit 330 for inputting the deformation, rigidity, young's modulus into the trained machine learning model to judge the activity of the bacteria.
By adopting the device, the force spectrums of the living bacteria and the dead bacteria are respectively obtained through an atomic force microscope, then the obtained force curves are subjected to post-treatment to extract the deformation, the rigidity and the Young modulus, and the data are input into a machine learning model, so that the activity of bacteria can be judged through the mechanical characteristic information of the surfaces of the bacteria. In addition, in the detection process, the probe of the atomic force microscope acts on the surface of the bacteria with a force of a Pi-ox grade, which is far smaller than the mechanical external force lethal to the bacteria, so that the bacteria cannot be damaged in the test, the in-situ detection can be carried out under the physiological condition, and the rapid and accurate analysis of the machine learning can be combined.
Further, referring to fig. 4, fig. 4 is a block diagram illustrating an exemplary configuration of a bacterial activity detection device 400 according to yet another embodiment of the present application.
As shown in fig. 4, the judging unit includes:
a building unit 410 for building an ensemble learning algorithm by using a stacking method;
a construction unit 420 for constructing a machine learning model according to an ensemble learning algorithm;
and an analysis unit 430 for inputting the deformation amount, the rigidity, and the young's modulus into the constructed machine learning model to judge the activity of the bacteria.
It should be understood that the units or modules described in the apparatuses 300-400 correspond to the various steps in the methods described with reference to fig. 1-2. Thus, the operations and features described above with respect to the methods are equally applicable to the apparatuses 300-400 and the units contained therein, and are not described in detail herein. The apparatus 300-400 may be implemented in advance in a browser or other security application of the electronic device, or may be loaded into a browser or security application of the electronic device by means of downloading, etc. The respective units in the apparatus 300-400 may cooperate with units in an electronic device to implement aspects of embodiments of the present application.
The method of the invention has been proved by experiments, and the scheme is feasible. The method is used for judging ultraviolet inactivated escherichia coli and live bacteria, and evaluating the performance of a model, wherein evaluation parameters are accuracy, precision, recall rate and F1 fraction, the accuracy is defined as the proportion of correctly classified samples to the total number of samples, the precision refers to the proportion of correctly classified positive samples to the number of samples judged as positive samples by a classifier, the recall rate refers to the proportion of correctly classified positive samples to the actual positive samples, and the F1 fraction is the harmonic mean value of the precision and the recall rate. The model performance of this example is shown in the following table, and it can be seen that the method has a higher accuracy rate of 95.19% in the test set.
Furthermore, we plotted the confusion matrix and subject operating characteristic curve (ROC curve) to evaluate the performance of the classifier, see fig. 10. The abscissa of the ROC curve is False Positive Rate (FPR), the ordinate is True Positive Rate (TPR), and the calculation methods of the FPR and the TPR are respectively as follows: fpr=fp/N, tpr=tp/P, where P is the number of true positive samples, N is the number of true negative samples, TP is the number of positive samples predicted by the classifier in P positive samples, and FP is the number of positive samples predicted in N negative samples. The AUC refers to the area under the ROC curve, the value can quantitatively reflect the model performance measured based on the ROC curve, the larger the AUC is, the better the classification performance of the classifier is, in this example, the AUC is 0.95, and the reliable result of the classifier is shown.
Referring now to FIG. 5, there is illustrated a schematic diagram of a computer system 500 suitable for use in implementing a terminal device or server of an embodiment of the present application.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output portion 507 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The drive 510 is also connected to the I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as needed so that a computer program read therefrom is mounted into the storage section 508 as needed.
In particular, according to embodiments of the present disclosure, the processes described above with reference to fig. 1-2 may be implemented as computer software programs. For example, embodiments of the present disclosure include a method of detecting bacterial activity comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for performing the method of fig. 1-2. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 509, and/or installed from the removable media 511.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present application may be implemented by software, or may be implemented by hardware. The described units or modules may also be provided in a processor, for example, as: a processor includes a first sub-region generation unit, a second sub-region generation unit, and a display region generation unit. The names of these units or modules do not constitute a limitation of the unit or module itself in some cases, and for example, the display area generating unit may also be described as "a unit for generating a display area of text from the first sub-area and the second sub-area".
As another aspect, the present application also provides a computer-readable storage medium, which may be a computer-readable storage medium contained in the foregoing apparatus in the foregoing embodiment; or may be a computer-readable storage medium, alone, that is not assembled into a device. The computer-readable storage medium stores one or more programs for use by one or more processors in performing the text generation method described herein as applied to transparent window envelopes.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the invention referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or equivalents thereof is possible without departing from the spirit of the invention. Such as the above-described features and technical features having similar functions (but not limited to) disclosed in the present application are replaced with each other.
Claims (5)
1. A method for detecting bacterial activity, the method comprising:
preparing a live bacterial liquid and a dead bacterial liquid;
the silicon nitride triangle cantilever probe on the atomic force microscope is vertically close to live bacteria and dead bacteria;
controlling the force exerted by the silicon nitride triangular cantilever probe on bacteria to reach a set value, then controlling the silicon nitride triangular cantilever probe to leave the living bacteria and the dead bacteria by an atomic force microscope, and simultaneously measuring and recording the force exerted by the probe in the process by the atomic force microscope so as to obtain a force curve, wherein the force curve is acquired in three areas of the same bacteria, the three areas are respectively the two ends and the center of the bacteria, and 3 points are randomly selected for recording the force curve in each area;
finding out the contact point between the probe tip and the bacterial surface from the force curve, wherein the deformation is the distance between the contact point and the force set point, namely the distance of probe pressing in;
obtaining stiffness from a slope fit in the force curve;
obtaining Young modulus through fitting the Sneddon model to the pressed-in section of the force curve;
an ensemble learning algorithm is established by adopting a stacking method, and a base model of the ensemble learning algorithm comprises four classifiers: extreme gradient lifting, random forest and self-adaptive lifting, wherein the meta model is a logistic regression algorithm;
constructing a machine learning model according to an integrated learning algorithm;
the deformation, stiffness and Young's modulus are input into a constructed machine learning model to judge the activity of bacteria.
2. The method for detecting bacterial activity according to claim 1, wherein the preparation of the live bacterial liquid and the dead bacterial liquid comprises:
performing oxygen plasma activation on a sterile glass bottom culture dish, then dropwise adding 200 mu L of polylysine solution with the concentration of 0.01%, washing the surface with deionized water after five minutes, and then drying with nitrogen to obtain a polylysine modified glass substrate;
3mL of the bacterial liquid is placed in a centrifuge tube, centrifuged at 5000rpm for five minutes, the uniform bacterial liquid is dripped on a glass substrate modified by polylysine for fixation, and after five minutes, the bacterial liquid is respectively washed three times with 1mL of phosphate buffer salt solution, and the non-fixed bacteria are removed.
3. A bacterial activity detection device, the device comprising:
the preparation unit is used for preparing live bacterial liquid and dead bacterial liquid;
the moving unit is used for vertically approaching the silicon nitride triangular cantilever probe on the atomic force microscope to living bacteria and dead bacteria;
the control unit is used for controlling the force exerted on bacteria by the silicon nitride triangular cantilever probe to reach a set value, then controlling the silicon nitride triangular cantilever probe to leave the living bacteria and the dead bacteria by the atomic force microscope, and simultaneously measuring and recording the force exerted by the probe in the process by the atomic force microscope so as to obtain a force curve, wherein the force curve is acquired in three areas of the same bacteria, the three areas are respectively the two ends and the center of the bacteria, and each area randomly selects 3 points to record the force curve; finding out the contact point between the probe tip and the bacterial surface from the force curve, wherein the deformation is the distance between the contact point and the force set point, namely the distance of probe pressing in; obtaining stiffness from a slope fit in the force curve; obtaining Young modulus through fitting the Sneddon model to the pressed-in section of the force curve;
an establishing unit, configured to establish an ensemble learning algorithm by adopting a stacking method, where a base model of the ensemble learning algorithm includes four kinds of classifiers: extreme gradient lifting, random forest and self-adaptive lifting, wherein the meta model is a logistic regression algorithm;
the building unit is used for building a machine learning model according to the integrated learning algorithm;
and the analysis unit is used for inputting the deformation, the rigidity and the Young modulus into the constructed machine learning model to judge the activity of the bacteria.
4. A computer device comprising a memory, a processor, and a memory controller stored on the memory
Computer program executable on a processor, characterized in that the processor implements the method according to any of claims 1-2 when executing the program.
5. A computer readable storage medium having stored thereon a computer program for:
the computer program implementing the method according to any of claims 1-2 when executed by a processor.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004125614A (en) * | 2002-10-02 | 2004-04-22 | Olympus Corp | Atomic force microscope |
| CN104655879A (en) * | 2015-03-05 | 2015-05-27 | 北京大学第三医院 | Method for detecting stiffness of cervical exfoliated cells by AFM (atomic force microscopy) |
| CN112410206A (en) * | 2020-09-03 | 2021-02-26 | 中国科学院深圳先进技术研究院 | Rapid detection system and method |
| WO2021076075A2 (en) * | 2019-10-14 | 2021-04-22 | İzmi̇r Yüksek Teknoloji̇ Ensti̇tüsü | Cell viability analysis and counting from holograms by using deep learning and appropriate lensless holographic microscope |
| JP2021071287A (en) * | 2019-10-29 | 2021-05-06 | 国立研究開発法人物質・材料研究機構 | Method and device for analyzing elastic property distribution of cell under test, and method and device for determining shape parameter of atomic force microscope probe |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110008801A1 (en) * | 2006-03-20 | 2011-01-13 | Worcester Polytechnic Institute | Methods for measuring cell-cell or cell-matrix adhesive forces and compounds for disrupting adhesive forces in biological systems |
| US20190138786A1 (en) * | 2017-06-06 | 2019-05-09 | Sightline Innovation Inc. | System and method for identification and classification of objects |
-
2021
- 2021-12-13 CN CN202111519297.9A patent/CN114414850B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004125614A (en) * | 2002-10-02 | 2004-04-22 | Olympus Corp | Atomic force microscope |
| CN104655879A (en) * | 2015-03-05 | 2015-05-27 | 北京大学第三医院 | Method for detecting stiffness of cervical exfoliated cells by AFM (atomic force microscopy) |
| WO2021076075A2 (en) * | 2019-10-14 | 2021-04-22 | İzmi̇r Yüksek Teknoloji̇ Ensti̇tüsü | Cell viability analysis and counting from holograms by using deep learning and appropriate lensless holographic microscope |
| JP2021071287A (en) * | 2019-10-29 | 2021-05-06 | 国立研究開発法人物質・材料研究機構 | Method and device for analyzing elastic property distribution of cell under test, and method and device for determining shape parameter of atomic force microscope probe |
| CN112410206A (en) * | 2020-09-03 | 2021-02-26 | 中国科学院深圳先进技术研究院 | Rapid detection system and method |
Non-Patent Citations (5)
| Title |
|---|
| Nanomechanical properties of dead or alive single-patterned bacteria;Aline Cerf et al.;《Langmuir》;第25卷(第10期);第5731-5736页 * |
| 原子力显微镜力谱技术及其在微观生物力学领域的应用;葛林;《力学进展》;第48卷;第461-540页 * |
| 原子力显微镜技术在细胞力学和病理学研究中的应用;王海燕 等;《激光生物学报》;第22卷(第2期);第97-102页 * |
| 基于原子力显微镜解析抗菌材料抗菌机理;金航帅;《中国优秀硕士学位论文全文数据库 工程科技I辑》(第02期);第49页 * |
| 杨游云 等.《Python广告数据挖掘与分析实战》.机械工业出版社,2021,第133页. * |
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