Signal detection method for exploring influence of histidine kinase inhibitor on bacterial drug resistance

Abstract

The invention provides a signal detection method for exploring the influence of a histidine kinase inhibitor on bacterial drug resistance, which comprises the following steps of: respectively acquiring images of a bacterial culture group added with the histidine kinase inhibitor, a culture group added with glucose and bacterial control and a control group; respectively carrying out feature point detection and marking on the culture group image and the control group image by using a feature point detection algorithm; traversing and counting the number of feature points of the culture group image and the control group image, calculating a bacterial growth change rate delta, and generating a corresponding bacterial drug resistance influence curve according to the sampling frequency and the corresponding bacterial growth change rate delta. Signal detection and analysis of the influence of the histidine kinase inhibitor on the bacterial drug resistance can be realized by using image recognition and feature point detection technologies, so that the influence of the inhibitor on the bacterial drug resistance is evaluated. Therefore, the whole steps are simplified, too many test equipment and process steps are not needed, the whole method is realized by depending on an electronic program, the test flow and cost are saved, and the drug resistance detection efficiency is accelerated.

Description

Technical Field

[0001] The present invention relates to the field of medical detection technology, and in particular to a signal detection method for exploring the effect of histidine kinase inhibitors on bacterial drug resistance, a signal detection device for exploring the effect of histidine kinase inhibitors on bacterial drug resistance, an electronic device and a computer-readable storage medium. Background Art

[0002] Bacterial drug resistance, also known as drug resistance, refers to the tolerance of bacteria to the effects of antibiotics. When bacteria come into contact with drugs multiple times, their sensitivity to the drugs decreases or even disappears, resulting in reduced or ineffective efficacy of the drugs against bacteria. Bacterial drug resistance can be divided into acquired resistance and natural resistance. Natural resistance is determined by bacterial chromosome genes and is passed down from generation to generation; acquired resistance is produced by bacteria changing their own metabolic pathways after coming into contact with antibiotics.

[0003] Bacterial resistance is the result of bacterial evolutionary selection, and the abuse of antibiotics has exacerbated this problem. The emergence of enzyme inhibitors provides a new way to address bacterial resistance, but larger-scale studies are still needed to evaluate their safety and effectiveness.

[0004] The emergence of bacterial resistance increases the difficulty of curing infectious diseases, forcing humans to find new ways to fight microbial infections. At the same time, it also threatens the effectiveness of drug treatments that humans rely on, making it more difficult to treat infections, or even impossible.

[0005] Bacterial resistance is an important problem facing the medical field, which requires the rational use of antibiotics such as enzyme inhibitors and the continuous exploration of new treatments and strategies to deal with it.

[0006] Enzyme inhibitors mainly block the β-lactamase produced by bacteria, which can render β-lactam antibiotics ineffective. Through enzyme inhibitors, antibacterial drugs can restore their effectiveness against drug-resistant bacteria. For example, histidine kinase inhibitors. The histidine kinase inhibitors currently under research include small molecule inhibitors against YycG of Staphylococcus epidermidis, and lead inhibitors of histidine kinase VicK of Streptococcus pneumoniae obtained through homology modeling and virtual screening.

[0007] The clinical exploration of the detection steps of bacterial resistance to histidine kinase inhibitors usually includes the following steps: Preparation phase: Obtain target bacteria, such as Staphylococcus aureus or Streptococcus pneumoniae, and culture to an appropriate concentration; ‌Inhibitor treatment‌: Add histidine kinase inhibitors to the bacterial culture medium and set up a control group; Drug resistance testing: Evaluate the effect of inhibitors on bacterial resistance by observing bacterial growth and determining the minimum inhibitory concentration (MIC).

[0008] There are usually two ways to detect the impact of bacterial resistance: First, the concentration of bacteria after the action of histidine kinase inhibitors is detected, such as using an electronic counter: counting by measuring the resistance change of the liquid in the small hole, with high accuracy, but requiring the bacterial suspension to be pure; or spectrophotometry: by measuring the absorbance of the bacterial suspension, the bacterial concentration is calculated according to the standard curve. However, a standard OD curve must be established first to ensure accuracy; Second, use drug-resistant gene chips to detect drug-resistant genes.

[0009] However, the detection steps and preparations involved in the above two methods are relatively complicated. For example, the method of using an electronic counter to determine the bacterial concentration mainly uses an electronic counter to detect and count bacteria passing through a specific small hole. The steps are as follows: ‌Preparation of bacterial suspension‌: First, extract bacteria from the sample to be tested and prepare an appropriate suspension; the concentration and purity of the suspension should be adjusted according to the requirements of the electronic counter and the purpose of the experiment; ‌Calibrate an electronic counter‌: Before using an electronic counter, it needs to be calibrated to ensure its accuracy and reliability; calibration usually involves testing with a bacterial suspension of known concentration and adjusting the settings of the counter to obtain an accurate reading; ‌Loading bacterial suspension‌: Load the prepared bacterial suspension into the sample chamber of the electronic counter. Make sure the suspension is evenly distributed and avoid bubbles or clogging of the small holes; ‌Start the electronic counter‌: Turn on the power of the electronic counter and start the counting program. The counter will begin to detect bacteria passing through the small hole and record the counting results; ‌Data recording and analysis‌.

[0010] Therefore, its preparation and operation steps as well as the process reagents involved are all relatively complicated, and care must be taken to avoid the generation of bubbles or clogging of the small holes of the electronic counter. Therefore, its operability and convenience are not high.

[0011] The drug-resistant gene chip involves: processing the collected samples to extract DNA; designing specific probes and amplification primers based on known drug-resistant gene sequences and preparing them into gene chips; using fluorescent polymerase chain reaction and other technologies to amplify the extracted DNA and hybridize it with the probes on the gene chip; signal detection and data analysis: using specific equipment to detect hybridization signals and analyze the signals to determine whether there are drug-resistant genes. Therefore, this method is more time-consuming, costly and labor-intensive, and the probe design is more complicated and takes a long time. Summary of the invention

[0012] In order to solve the technical problems existing in the prior art, the present invention provides the following technical solutions: In one aspect, a signal detection method for exploring the effect of histidine kinase inhibitors on bacterial drug resistance is provided, the method being implemented by an electronic device, the method comprising: S1. For the bacterial culture group with histidine kinase inhibitor added and the control group with glucose and bacteria added, the corresponding culture group image signals and control group image signals are collected according to the sampling frequency and uploaded to the backend server; S2, receiving and processing the image signal of the culture group and the image signal of the control group through the background server, and generating corresponding images of the culture group and the control group; S3, using a feature point detection algorithm to perform feature point detection and feature point marking on the culture group image and the control group image respectively; S4, traverse and count the number of feature points of the culture group image and the control group image, and calculate the bacterial growth change rate δ: δ = (Sn-S n-1 ) / (Sm-S m-1 ), Among them, Sn and S n-1 are the number of feature points counted before and after sampling the culture group image, Sm and S m-1 are the number of feature points counted by sampling the control group image before and after respectively; S5. Generate a corresponding bacterial resistance impact curve according to the sampling frequency and the corresponding bacterial growth change rate δ.

[0013] Preferably, S1, for the bacterial culture group with histidine kinase inhibitor added and the control group with glucose and bacteria added, the corresponding culture group image signals and control group image signals are collected according to the sampling frequency and uploaded to the backend server, including: Set the sampling frequency of the electron microscope; According to the sampling frequency, the dosage and addition time of the added histidine kinase inhibitor and glucose are recorded, and uploaded and saved to the backend server; Using the electron microscope, respectively collecting the image signals of the culture group and the image signals of the control group and uploading them to the backend server; The background server records the information and image signals recorded and collected each time.

[0014] Preferably, the bacterium is Staphylococcus aureus or Streptococcus pneumoniae.

[0015] Preferably, the histidine kinase inhibitor is a small molecule inhibitor against YycG of Staphylococcus epidermidis or a lead inhibitor against histidine kinase VicK of Streptococcus pneumoniae.

[0016] Preferably, S3, using a feature point detection algorithm, respectively detecting and marking feature points on the culture group image and the control group image, including: Preprocessing the culture group image and the control group image to delete edge bacterial spots and incomplete bacterial spots in the images; Using a feature point detection algorithm, respectively identifying and marking bacterial feature points in the culture group image and the control group image, and generating a first feature-marked image and a second feature-marked image corresponding to the culture group image and the control group image; The first feature-marked image and the second feature-marked image are output and saved.

[0017] Preferably, in step S4, traversing and counting the number of feature points of the culture group image and the control group image includes: Traversing the first feature-marked image, identifying and counting the number of feature points in the first feature-marked image, recorded as Sn; Traversing the second feature-marked image, identifying and counting the number of feature points in the second feature-marked image, recorded as Sm; According to the above method, the number of feature points at each frequency is recorded and saved in turn.

[0018] Preferably, the feature point detection algorithm adopts SIFT, SURF, ORB or Harris feature point detection algorithm.

[0019] On the other hand, a signal detection device for exploring the effect of histidine kinase inhibitors on bacterial drug resistance is provided, wherein the signal detection device for exploring the effect of histidine kinase inhibitors on bacterial drug resistance is used to implement the signal detection method for exploring the effect of histidine kinase inhibitors on bacterial drug resistance described above, and the device comprises: An image acquisition device is used to respectively acquire the image signals of the bacterial culture group with histidine kinase inhibitor added and the control group with glucose and bacteria added according to the sampling frequency and upload them to the backend server; Backend server, including: An image processing module, used for receiving and processing the image signal of the culture group and the image signal of the control group through the background server, and generating corresponding images of the culture group and the control group; A feature point detection module, used to use a feature point detection algorithm to perform feature point detection and feature point marking on the culture group image and the control group image respectively; The image feature point statistics module is used to traverse and count the number of feature points of the culture group image and the control group image, and calculate the bacterial growth change rate δ: δ = (Sn-S n-1 ) / (Sm-S m-1 ), Among them, Sn and S n-1 are the number of feature points counted before and after sampling the culture group image, Sm and S m-1 are the number of feature points counted by sampling the control group image before and after respectively; A curve generation module is used to generate a corresponding bacterial resistance impact curve according to the sampling frequency and the corresponding bacterial growth change rate δ; Backend database, used for data storage; The image processing module, feature point detection module, image feature point statistics module, curve generation module and backend database are all deployed on a backend server; The image acquisition device is communicatively connected with the background server.

[0020] On the other hand, an electronic device is provided, comprising: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the signal detection methods for exploring the effect of histidine kinase inhibitors on bacterial resistance is implemented.

[0021] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned signal detection methods for exploring the effect of histidine kinase inhibitors on bacterial resistance.

[0022] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: The present invention collects the corresponding culture group image signals and control group image signals according to the sampling frequency for the bacterial culture group with the addition of histidine kinase inhibitor and the control group with the addition of glucose and bacteria, and processes the image signals to generate the corresponding culture group image and control group image; uses the feature point detection algorithm to detect the feature points of the culture group image and the control group image and mark the feature points; traverses and counts the number of feature points of the culture group image and the control group image, and calculates the bacterial growth change rate δ, and generates the corresponding bacterial resistance influence curve according to the sampling frequency and the corresponding bacterial growth change rate δ. The signal detection and analysis of the effect of histidine kinase inhibitors on bacterial resistance can be realized by using image recognition and feature point detection technology, and the effect of histidine kinase inhibitors on bacterial resistance can be explored by constructing the bacterial resistance influence curve, so as to evaluate the effect of the inhibitors on bacterial resistance. Therefore, the overall steps are simplified and do not require too many test equipment and process steps. The overall process relies on electronic programs to save test processes and costs, and accelerates the efficiency of drug resistance detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 This is a flow chart of a signal detection method for exploring the effect of histidine kinase inhibitors on bacterial resistance provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of image marking of bacterial feature points identified in a culture group image provided by an embodiment of the present invention; Figure 3 is a schematic diagram of image marking of bacterial feature points identified in a control group image provided by an embodiment of the present invention; Figure 4 It is a schematic diagram of a growth change curve of bacteria in a culture group and a control group provided in an embodiment of the present invention; Figure 5 This is a block diagram of a signal detection device for exploring the effect of histidine kinase inhibitors on bacterial resistance provided by an embodiment of the present invention; Figure 6 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0026] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0027] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0028] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0029] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0030] In the present invention, the steps of bacterial culture can refer to the bacterial culture test in the existing laboratory, and this embodiment will not be repeated. The corresponding experimental equipment, reagents, dosages, etc. involved can be completed by laboratory staff according to general experimental steps and laboratory requirements. This embodiment will not repeat the experimental steps.

[0031] The present invention mainly performs software processing on the collected bacterial culture images and generates corresponding bacterial resistance comparison curves, so as to study and explore the influence of histidine kinase inhibitors on bacterial resistance.

[0032] The embodiment of the present invention provides a signal detection method for exploring the effect of histidine kinase inhibitors on bacterial resistance. The method can be implemented by an electronic device, which can be a terminal or a server. Figure 1 The signal detection method flow chart for exploring the effect of histidine kinase inhibitors on bacterial resistance is shown in the figure. The processing flow of the method may include the following steps: S1. For the bacterial culture group with histidine kinase inhibitor added and the control group with glucose and bacteria added, the corresponding culture group image signals and control group image signals are collected according to the sampling frequency and uploaded to the backend server; S2, receiving and processing the image signal of the culture group and the image signal of the control group through the background server, and generating corresponding images of the culture group and the control group; S3, using a feature point detection algorithm to perform feature point detection and feature point marking on the culture group image and the control group image respectively; S4, traverse and count the number of feature points of the culture group image and the control group image, and calculate the bacterial growth change rate δ: δ = (Sn-S n-1 ) / (Sm-S m-1 ), Among them, Sn and S n-1 are the number of feature points counted before and after sampling the culture group image, Sm and S m-1 are the number of feature points counted by sampling the control group image before and after respectively; S5. Generate a corresponding bacterial resistance impact curve according to the sampling frequency and the corresponding bacterial growth change rate δ.

[0033] The main steps for testing bacterial resistance to histidine kinase inhibitors are: ‌Preparation phase‌: Obtain the target bacteria, such as Staphylococcus aureus or Streptococcus pneumoniae, and culture them to an appropriate concentration.

[0034] ‌Inhibitor treatment‌: Add histidine kinase inhibitors to the bacterial culture medium and set up a control group.

[0035] Drug resistance testing: Evaluate the effect of inhibitors on bacterial resistance by observing bacterial growth and determining the minimum inhibitory concentration (MIC).

[0036] Steps for detecting bacterial resistance signals‌ Signal capture: Use an electron microscope to detect and collect electron microscopic image signals of each group of bacteria and upload them to the backend server (computer).

[0037] ‌Data Analysis‌: Analyze the image signal and process it into the corresponding image. Through image recognition and feature point detection technology, mark the change data of the number of bacteria points with normal growth and inhibited growth in the image, compare and analyze the inhibitory mechanism of histidine kinase inhibitors of different doses and types on bacteria, and find the optimal concentration of histidine kinase inhibitors. Through the above feature point comparison and analysis, explore the inhibitory effect of histidine kinase inhibitors on bacterial growth, achieve rapid detection, and simplify the experimental steps.

[0038] Image recognition technology is used to count the number of feature points in the image, and the corresponding bacterial growth curve is generated based on the statistical results. Combined with the bacterial growth change rate, the bacterial resistance influence curve can be further fitted.

[0039] Combined with Figure 5 The device shown. After the bacteria are cultured, the corresponding bacterial sample slides can be constructed and placed under an electron microscope for image acquisition. And some intelligent electron microscopes (such as KWONG KUK Optics Valley Times 4K High-Definition Microscope KK312, Osmicron Industrial Electron Microscope AO-HD228S, Walinova Electron Microscope, which can take pictures and synchronously transmit image sampling signals through their data ports) can be used to take pictures and send them to the computer after being processed by their controller for further in-depth processing of the image. In this example, the model of the intelligent device used by the electron microscope can be determined by the experimental researchers, and the data communication between the electron microscope and the computer can upload images according to the communication port supported by the device.

[0040] On the background server, that is, the computer, corresponding image processing, feature recognition and statistics, as well as curve generation systems are arranged to realize the corresponding bacterial resistance detection and analysis service functions. For details, please refer to the description below.

[0041] After the image signal is collected, the background processes the image signal data and generates the corresponding image. Next, feature point detection in the image will be performed.

[0042] Using image recognition technology to count the number of feature points in an image usually involves the following steps: 1. Image preprocessing: Perform necessary preprocessing on the input image, such as grayscale, denoising, contrast enhancement, etc., to improve image quality and highlight feature points.

[0043] 2. Feature point detection: Use feature point detection algorithms (such as SIFT, SURF, ORB, Harris corner detection, etc.) to identify feature points in the image. These algorithms can locate points with significant local structural changes in the image, such as corners, edge points, points in texture-rich areas, etc.

[0044] 3. Feature point description: Describe the detected feature points and generate feature vectors or descriptors. These descriptors usually contain local image information around the feature points, such as gradient direction, intensity change, etc.

[0045] ‌Feature point matching (optional)‌: If you need to match feature points in multiple images, you can use a feature point matching algorithm (such as the nearest neighbor matching algorithm based on Euclidean distance, cosine similarity, etc.). This step is not necessary when only counting the number of feature points.

[0046] 4. Count the number of feature points: Iterate through the list of detected feature points and count their number. This can be achieved with a simple counting operation.

[0047] 5. Post-processing and verification (operated by experimenters): Post-process the statistical results, such as removing repeated feature points, verifying the validity of feature points, etc. This step can ensure the accuracy and reliability of the statistical results.

[0048] 6. Output results: Output the counted number of feature points in an appropriate form, such as displaying it on an image, saving it to a file, or printing it to a console.

[0049] In addition, deep learning methods in image recognition technology (such as convolutional neural networks) can also be used for feature point detection, but they usually require a lot of training data and computing resources. In simple tasks that only count the number of feature points, traditional feature point detection algorithms may be more efficient and practical.

[0050] Here, electron microscopic images of bacteria (the type of bacteria in this embodiment) can be used in advance (the morphology needs to be complete to facilitate the model identifier morphology, texture and other image features), and CNN can be used to identify and extract bacterial features in the image, so as to extract image features such as the morphology of Staphylococcus YycG or Streptococcus pneumoniae, and construct a corresponding feature set for CNN model training. Subsequently, the CNN model can be used to identify and mark bacteria in the images collected in the background, marking all bacteria with complete morphology on the image for easy counting. This is the model training principle of CNN, which will not be elaborated in this embodiment.

[0051] like Figure 2 The figure below shows the image marking diagram of the bacterial feature points identified in the culture group image. The bacterial feature points in the culture group image are detected using the feature point detection algorithm, and the bacterial feature points are marked to facilitate subsequent quantity statistics. Note: The black circles in the figure are marked bacterial points (because the magnification under the microscope is different, the display clarity is different. It needs to be displayed according to the magnification selected by the user).

[0052] Figure 3 This is a schematic diagram of image marking of bacterial feature points identified in a control group image provided by an embodiment of the present invention. The bacterial feature points in the image (that is, the feature points where the bacteria are located) are detected and marked in the same manner as the detection method of the culture group. By comparison, it can be found that the bacterial detection points in the control group are more dense, indicating that the bacteria reproduce and grow rapidly without inhibition.

[0053] To count the feature points in the graph, you can use the detection algorithm to detect and mark the knowledge and perform statistics, or you can use the traversal algorithm to count the number of feature points in the graph, for example: ‌Initialize counter‌: Create a counter variable to record the number of feature points that meet the conditions. The initial value is set to 0.

[0054] ‌Choose a traversal algorithm‌: Choose an appropriate traversal algorithm based on the type of graph and your requirements. For undirected or directed graphs, you can use depth-first search (DFS) or breadth-first search (BFS).

[0055] ‌Prepare data structure‌: If using DFS, prepare a stack (the stack can be implicitly implemented using recursive calls).

[0056] If using BFS, prepare a queue.

[0057] A collection or array is also needed to record the nodes that have been visited to avoid repeated visits.

[0058] ‌Start traversal‌: Start from a starting node in the graph (if it is a connected graph, you can start from any node; if it is a disconnected graph, you need to make sure to traverse all nodes, and you may need to start a traversal from each unvisited node).

[0059] Mark the starting node as visited and check if it satisfies the criteria for being a feature point. If so, increment the counter.

[0060] ‌Traverse adjacent nodes‌: For the current node, traverse all its adjacent nodes (according to the adjacency list or adjacency matrix).

[0061] For each neighboring node, if it has not been visited, mark it as visited and check if it satisfies the condition of the feature point. If so, increase the counter.

[0062] Depending on the traversal algorithm used (DFS or BFS), adjacent nodes are added to a stack (DFS) or queue (BFS) for subsequent access.

[0063] Repeated traversal: Repeat step 5 until the stack or queue is empty, indicating that all reachable nodes have been visited.

[0064] Check for disconnected graphs: If the graph is disconnected, make sure to traverse all nodes in the graph. This can be done by maintaining a set of unvisited nodes and checking whether the set is empty after each traversal.

[0065] If the set is not empty, select an unvisited node from the set as the new starting node and repeat the traversal process.

[0066] ‌Return results‌: After the traversal is completed, the value in the counter is the number of feature points in the graph that meet the conditions.

[0067] Figure 4 It is a schematic diagram of a growth change curve of bacteria in a culture group and a control group provided by an embodiment of the present invention. The number of characteristic points counted after each sampling can be used to construct a curve according to (frequency, number of characteristic points). The data sampled and uploaded by the device can be processed by a computer to automatically fit the corresponding coordinates and fit the curve.

[0068] from Figure 4 It can be seen that as time goes by (frequency increases), the number of bacteria in the culture group is affected by the histidine kinase inhibitor and gradually decreases; the opposite is true for the control group.

[0069] The before-after change rate of the corresponding group can be calculated, and the change in the ratio of the change rate of the culture group to the control group following the change in frequency (the changing trend of the bacterial growth change rate δ) can be counted, and the corresponding curve can be fitted as the bacterial resistance impact curve (the specific fitting can be operated by the administrator), which is convenient for observing the trend of the bacterial resistance impact.

[0070] The bacterial growth rate δ is calculated as: The number of feature points of the culture group image and the control group image is traversed and counted, and the bacterial growth change rate δ is calculated: δ = (Sn-S n-1 ) / (Sm-S m-1 ), Among them, Sn and S n-1 are the number of feature points counted before and after sampling the culture group image, Sm and S m-1 are respectively the number of feature points counted before and after sampling the control group image.

[0071] The data of the previous and subsequent calculations can be read from the background, so the bacterial growth change rate δ can be calculated and the trend change curve of bacterial inhibition growth can be fitted, which is convenient for analyzing and studying its mechanism.

[0072] The above steps can be performed on several groups of culture groups and control groups with different doses, bacteria or histidine kinase inhibitors, and can explore the inhibitory effects of different doses of histidine kinase inhibitors on bacteria, the inhibitory effects of different inhibitor types on bacteria, and the analysis of the immune effects of different bacteria on histidine kinase inhibitors. The corresponding data can be implemented in the above manner.

[0073] Preferably, S1, for the bacterial culture group with histidine kinase inhibitor added and the control group with glucose and bacteria added, the corresponding culture group image signals and control group image signals are collected according to the sampling frequency and uploaded to the backend server, including: Set the sampling frequency of the electron microscope; According to the sampling frequency, the dosage and addition time of the added histidine kinase inhibitor and glucose are recorded, and uploaded and saved to the backend server; Using the electron microscope, respectively collecting the image signals of the culture group and the image signals of the control group and uploading them to the backend server; The background server records the information and image signals recorded and collected each time.

[0074] The sampling frequency of the electron microscope is set by the user. Some intelligent electron microscopes can input parameters and automatically complete the corresponding sampling. Therefore, automated or semi-automated bacterial resistance detection and analysis can be achieved.

[0075] Preferably, the bacterium is Staphylococcus aureus or Streptococcus pneumoniae.

[0076] Preferably, the histidine kinase inhibitor is a small molecule inhibitor against YycG of Staphylococcus epidermidis or a lead inhibitor against histidine kinase VicK of Streptococcus pneumoniae.

[0077] Preferably, S3, using a feature point detection algorithm, respectively detecting and marking feature points on the culture group image and the control group image, including: Preprocessing the culture group image and the control group image to delete edge bacterial spots and incomplete bacterial spots in the images; Using a feature point detection algorithm, respectively identifying and marking bacterial feature points in the culture group image and the control group image, and generating a first feature-marked image and a second feature-marked image corresponding to the culture group image and the control group image; The first feature-marked image and the second feature-marked image are output and saved.

[0078] Preferably, in step S4, traversing and counting the number of feature points of the culture group image and the control group image includes: Traversing the first feature-marked image, identifying and counting the number of feature points in the first feature-marked image, recorded as Sn; Traversing the second feature-marked image, identifying and counting the number of feature points in the second feature-marked image, recorded as Sm; According to the above method, the number of feature points at each frequency is recorded and saved in turn.

[0079] Preferably, the feature point detection algorithm adopts SIFT, SURF, ORB or Harris feature point detection algorithm.

[0080] See the above description for details.

[0081] Therefore, the present invention is completed based on an electron microscope and a background server, the system composition is simplified, the process steps of bacterial resistance detection and analysis are reduced, and the detection and analysis efficiency is greatly improved.

[0082] Figure 5 The present invention is a block diagram of a signal detection device for exploring the effect of histidine kinase inhibitors on bacterial resistance according to an exemplary embodiment, wherein the device comprises: An image acquisition device is used to respectively acquire the image signals of the bacterial culture group with histidine kinase inhibitor added and the control group with glucose and bacteria added according to the sampling frequency and upload them to the backend server; Backend server, including: An image processing module, used for receiving and processing the image signal of the culture group and the image signal of the control group through the background server, and generating corresponding images of the culture group and the control group; A feature point detection module, used to use a feature point detection algorithm to perform feature point detection and feature point marking on the culture group image and the control group image respectively; The image feature point statistics module is used to traverse and count the number of feature points of the culture group image and the control group image, and calculate the bacterial growth change rate δ: δ = (Sn-S n-1 ) / (Sm-S m-1 ), Among them, Sn and S n-1 are the number of feature points counted before and after sampling the culture group image, Sm and S m-1 are the number of feature points counted by sampling the control group image before and after respectively; A curve generation module is used to generate a corresponding bacterial resistance impact curve according to the sampling frequency and the corresponding bacterial growth change rate δ; Backend database, used for data storage; The image processing module, feature point detection module, image feature point statistics module, curve generation module and backend database are all deployed on a backend server; The image acquisition device is communicatively connected with the background server.

[0083] Please understand the functions and interaction processes of the above-mentioned image acquisition device (intelligent electron microscope) and the background server in conjunction with the above-mentioned method, and will not be repeated here.

[0084] Figure 6 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 6 As shown, the electronic device may include the above Figure 3 The signal detection device for exploring the effect of histidine kinase inhibitors on bacterial drug resistance is shown. Optionally, the electronic device 410 may include a first processor 2001 .

[0085] Optionally, the electronic device 410 may further include a memory 2002 and a transceiver 2003 .

[0086] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0087] Combine the following Figure 6 The components of the electronic device 410 are described in detail: The first processor 2001 is the control center of the electronic device 410, and may be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or may be application specific integrated circuits (ASICs), or may be one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0088] Optionally, the first processor 2001 can execute various functions of the electronic device 410 by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0089] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 6 CPU0 and CPU1 are shown in FIG.

[0090] In a specific implementation, as an embodiment, the electronic device 410 may also include multiple processors, such as Figure 6 The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0091] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0092] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently and access the first processor 2001 through the interface circuit ( Figure 6 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0093] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0094] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 6 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0095] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently and communicate with the first processor 2001 through the interface circuit ( Figure 6 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0096] It should be noted that Figure 6 The structure of the electronic device 410 shown in the figure does not constitute a limitation on the router, and the actual knowledge structure recognition device may include more or fewer components than those shown in the figure, or combine certain components, or arrange the components differently.

[0097] In addition, the technical effects of the electronic device 410 can refer to the technical effects of the signal detection method for exploring the effect of histidine kinase inhibitors on bacterial resistance described in the above method embodiment, and will not be repeated here.

[0098] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0099] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0100] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0101] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0102] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0103] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0104] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0105] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0106] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0107] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0108] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0109] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0110] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A signal detection method for exploring the effect of histidine kinase inhibitors on bacterial resistance, characterized in that: The method comprises: S1. For the bacterial culture group with histidine kinase inhibitor added and the control group with glucose and bacteria added, the corresponding culture group image signals and control group image signals are collected according to the sampling frequency and uploaded to the backend server; S2, receiving and processing the image signal of the culture group and the image signal of the control group through the background server, and generating corresponding images of the culture group and the control group; S3, using a feature point detection algorithm to perform feature point detection and feature point marking on the culture group image and the control group image respectively; S4, traverse and count the number of feature points of the culture group image and the control group image, and calculate the bacterial growth change rate δ: δ=(Sn-S n-1 ) / (Sm-S m-1 ), Among them, Sn and S n-1 are the number of feature points counted before and after sampling the culture group image, Sm and S m-1 are the number of feature points counted by sampling the control group image before and after respectively; S5. Generate a corresponding bacterial resistance impact curve according to the sampling frequency and the corresponding bacterial growth change rate δ.

2. The signal detection method for exploring the effect of histidine kinase inhibitors on bacterial resistance according to claim 1, characterized in that: S1. For the bacterial culture group with histidine kinase inhibitor added and the control group with glucose and bacteria added, the corresponding culture group image signals and control group image signals are collected according to the sampling frequency and uploaded to the backend server, including: Set the sampling frequency of the electron microscope; According to the sampling frequency, the dosage and addition time of the added histidine kinase inhibitor and glucose are recorded, and uploaded and saved to the backend server; Using the electron microscope, respectively collecting the image signals of the culture group and the image signals of the control group and uploading them to the backend server; The background server records the information and image signals recorded and collected each time.

3. The signal detection method for exploring the effect of histidine kinase inhibitors on bacterial drug resistance according to claim 1, characterized in that: The bacteria used are Staphylococcus aureus or Streptococcus pneumoniae.

4. The signal detection method for exploring the effect of histidine kinase inhibitors on bacterial resistance according to claim 1, characterized in that: The histidine kinase inhibitor is a small molecule inhibitor against YycG of Staphylococcus epidermidis or a lead inhibitor against histidine kinase VicK of Streptococcus pneumoniae.

5. The signal detection method for exploring the effect of histidine kinase inhibitors on bacterial drug resistance according to claim 1, characterized in that: S3, using a feature point detection algorithm to perform feature point detection and feature point marking on the culture group image and the control group image respectively, including: Preprocessing the culture group image and the control group image to delete edge bacterial spots and incomplete bacterial spots in the images; Using a feature point detection algorithm, respectively identifying and marking bacterial feature points in the culture group image and the control group image, and generating a first feature-marked image and a second feature-marked image corresponding to the culture group image and the control group image; The first feature-marked image and the second feature-marked image are output and saved.

6. The signal detection method for exploring the effect of histidine kinase inhibitors on bacterial drug resistance according to claim 5, characterized in that: In step S4, traversing and counting the number of feature points of the culture group image and the control group image includes: Traversing the first feature-marked image, identifying and counting the number of feature points in the first feature-marked image, recorded as Sn; Traversing the second feature-marked image, identifying and counting the number of feature points in the second feature-marked image, recorded as Sm; According to the above method, the number of feature points at each frequency is recorded and saved in turn.

7. The signal detection method for exploring the effect of histidine kinase inhibitors on bacterial drug resistance according to claim 5, characterized in that: The feature point detection algorithm adopts SIFT, SURF, ORB or Harris feature point detection algorithm.

8. A signal detection device for exploring the effect of a histidine kinase inhibitor on bacterial drug resistance, the signal detection device for exploring the effect of a histidine kinase inhibitor on bacterial drug resistance is used to implement the signal detection method for exploring the effect of a histidine kinase inhibitor on bacterial drug resistance as claimed in any one of claims 1 to 7, characterized in that: The device comprises: An image acquisition device is used to respectively acquire the image signals of the bacterial culture group with histidine kinase inhibitor added and the control group with glucose and bacteria added according to the sampling frequency and upload them to the backend server; Backend server, including: An image processing module, used for receiving and processing the image signal of the culture group and the image signal of the control group through the background server, and generating corresponding images of the culture group and the control group; A feature point detection module, used to use a feature point detection algorithm to perform feature point detection and feature point marking on the culture group image and the control group image respectively; The image feature point statistics module is used to traverse and count the number of feature points of the culture group image and the control group image, and calculate the bacterial growth change rate δ: δ=(Sn-S n-1 ) / (Sm-S m-1 ), Among them, Sn and S n-1 are the number of feature points counted before and after sampling the culture group image, Sm and S m-1 are the number of feature points counted by sampling the control group image before and after respectively; A curve generation module is used to generate a corresponding bacterial resistance impact curve according to the sampling frequency and the corresponding bacterial growth change rate δ; Backend database, used for data storage; The image processing module, feature point detection module, image feature point statistics module, curve generation module and backend database are all deployed on a backend server; The image acquisition device is communicatively connected with the background server.

9. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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