Feline herpesvirus type 1 monitoring and evaluation platform and its application

Abstract

The present invention provides a feline herpesvirus (FHV) type 1 monitoring and evaluation platform and application, including an FHV antigen selection module, a monoclonal antibody preparation module, a serum sample collection module, a reaction detection module, a diagnosis and judgment module, and a verification experiment module. By selecting appropriate antigens and obtaining monoclonal antibodies from mice, and combining immunological techniques to detect the level of FHV monoclonal antigens in cat serum samples, the system can assist in the diagnosis and monitoring of the disease, and provide a basis for the treatment of the disease.

Description

Technical Field

[0001] The invention relates to the field of intelligent medical treatment and data processing, and specifically to a feline herpesvirus type 1 monitoring and evaluation platform and its application. Background Art

[0002] Feline herpesvirus type 1 is a common pathogen that causes upper respiratory tract infections in cats. Cats infected with feline herpesvirus usually show symptoms such as sneezing, nasal congestion, tearing eyes, and oral ulcers. The virus is transmitted through direct contact with infected objects and airborne droplets, and is especially prevalent in multi-cat environments. Diagnosis is mainly through symptom analysis and laboratory testing. In the field of veterinary medicine, research on feline herpesvirus involves the biological characteristics of the virus, the virus replication process, and the host immune response. By exploring the interaction between feline herpesvirus and the host immune system, we can better understand how the virus infects the host and causes disease. Research on FHV-1 will help improve prevention and control measures for respiratory infections in cats and reduce the harm caused by the disease. Further exploration of chemotherapy research on feline herpesvirus, exploration of new treatments and drugs, and provide better health protection for cats. Summary of the invention

[0003] In view of the above problems, the present invention aims to provide a feline herpesvirus type 1 monitoring and evaluation platform and application to solve the problems raised in the above background technology.

[0004] The purpose of the present invention is achieved through the following technical solutions:

[0005] The invention provides a feline herpesvirus type 1 monitoring and evaluation platform and application, comprising an FHV antigen selection module, a monoclonal antibody preparation module, a serum sample collection module, a reaction detection module, a diagnosis and judgment module and a verification experiment module. The FHV antigen selection module selects an FHV antigen suitable for immunizing mice and ensures that it can induce the production of highly efficient monoclonal antibodies. The monoclonal antibody preparation module obtains specific FHV monoclonal antibodies from the immunized mice and ensures that they have high specificity and affinity. The serum sample collection module collects blood samples of cat patients as sample sources for subsequent experiments. The reaction detection module detects the FHV monoclonal antibodies in the cat serum samples by using an enzyme-linked immunosorbent assay to determine the presence and level of the FHV monoclonal antibodies. The diagnosis and judgment module determines whether the cat suffers from comorbid symptoms according to the detection results obtained by the reaction detection module, and performs disease monitoring and treatment. The verification experiment module verifies the effectiveness and feasibility of the monitoring system in clinical practice.

[0006] Furthermore, the FHV antigen selection module selects FHV antigens suitable for immunizing mice and ensures that they can induce the production of highly efficient monoclonal antibodies. In order to select suitable FHV antigens, the antigens come from natural isolates of FHV and synthetic recombinant antigens.

[0007] Furthermore, the operation process of the FHV antigen selection module includes the following steps:

[0008] (1) Analyze and identify the species, subtypes, and pathogenic strains of FHV to determine the appropriate source of antigens;

[0009] (2) Designing an appropriate antigen expression system based on the characteristics of the selected antigen, including selecting a suitable expression vector and host cell;

[0010] (3) Express and purify the antigen to ensure the stability of its structure and activity;

[0011] (4) The specificity of the antigen is detected and verified through biochemical and immunological methods to ensure that it can effectively stimulate the immune mice to produce monoclonal antibodies.

[0012] Furthermore, the monoclonal antibody preparation module obtains specific FHV monoclonal antibodies from immunized mice and ensures that they have high specificity and affinity. According to the specificity and immunogenicity of the FHV antigen, a suitable immunized mouse strain is selected, and a suitable immunization scheme is designed to induce a specific antibody response. The immunized mouse strain selects BALB / c mice as the experimental animal strain, and BALB / c mice have good immunoreactivity and easy operation; according to the weight and immunization scheme of the immunized mice, a suitable antigen immunization plan is designed, and the stability and immunogenicity of the antigen are ensured; the FHV antigen is injected into the immunized mice through an appropriate route to induce them to produce an antigen-specific immune response; after antigen immunization, the spleen cells of the immunized mice are collected as raw materials for subsequent monoclonal antibody preparation; hybridoma cells are obtained using cell fusion technology, and these hybridoma cells have stable monoclonal properties; the hybridoma cells are screened and identified, and the hybridoma cell lines that produce FHV-specific monoclonal antibodies are screened, and amplified and cultured.

[0013] Furthermore, the serum sample collection module collects blood samples from cat patients as a sample source for subsequent experiments. The source of serum samples is clinical patients (cats). In the present invention, cat patients are used as sample sources, and blood samples from patients are collected for subsequent immunological testing and diagnosis. For the sample collection method, conventional blood collection techniques suitable for cats are selected, including jugular vein blood collection and tail vein blood collection, to ensure that sufficient serum samples are collected for subsequent experiments. At the same time, attention should be paid to the operating specifications and sanitary conditions during blood collection to avoid contamination and sample loss; secondly, the operating procedures of the serum sample collection module include: determining the clinical information and medical history of the cat patient, including age, gender, and disease course; selecting a suitable blood collection time and blood collection site, including blood collection when the cat patient is fasting or full, and selecting a suitable blood collection site; performing blood collection operations, including using appropriate blood collection needles and blood collection tubes to avoid excessive blood drawing and sample contamination; placing the collected blood sample in an appropriate blood collection tube, and marking the sample information, including the cat patient number and blood collection time, for subsequent experimental tracing and diagnosis.

[0014] Furthermore, the serum sample collection module processes the collected samples and defines the matrix S to represent the evaluation of skill level and equipment performance, Q op Indicates the quality assessment of blood collection operation, including: Where T is the number of time points, M is the number of blood sampling sites, and the matrix S represents the skill level and equipment performance under different operating conditions. ij ∈S,S ij is the skill level and equipment performance of the jth blood collection site at the i-th time point. Then, the dynamic relationship between time and temperature during sample processing is established. The differential equation is used to represent the change of sample temperature T(t) after processing with time, and the influence of ambient temperature on sample temperature is considered. in, represents the change of sample temperature over time after treatment, represents the sample processing rate constant, t represents time, T 环境 Assume that the ambient temperature is the ambient temperature, the sample storage conditions are optimized, the optimal storage conditions are formulated, and the temperature and humidity factors are considered. Let C be the parameter set of the storage conditions:

[0015]

[0016] Among them, [t1, t2] is the time window, C represents the parameter set of the saving condition, T 目标 is the sample temperature, H(t) is the sample humidity, H 目标The target sample humidity is the sample storage condition optimized by minimizing the objective function J(C). The optimal parameter set C is solved by the gradient descent method to minimize the objective function. The most suitable blood collection site is selected based on the physiological characteristics of cats. In order to be able to select the probability of a specific blood collection site for cat patients, the selection probability model is established as follows:

[0017]

[0018] Among them, P 选择 represents the probability of selecting a specific blood sampling site, I ip , I kq Both represent feature matrices, W jp , W jq Both represent weight matrices, α p , α q For I ip The regulatory factors and β p , β q W jp By evaluating the physiological characteristics of different parts, the most suitable blood collection site is selected. Since the blood collection rate during the collection process will affect the sample quality, a differential equation is used to describe the change of blood collection rate R(t) over time: Where R(t) represents the change of blood collection rate over time, and It is a parameter that controls the growth of the collection rate. For the risks and complications that may occur during the blood collection process, this system uses a probability model to evaluate the probability of risk events and calculate the expected risk value Risk:

[0019]

[0020] Among them, P i Indicates the probability of occurrence of various risk events, Severity i Severity k It indicates the severity of various risk events, u and v are contribution factors. Due to the temperature changes of cat patients' blood samples during transportation, the temperature control of the sample transportation process is established. 运输 (t) Time-varying model:

[0021]

[0022] Where T 运输 (t) represents the change of sample temperature over time during transportation, T0 represents the initial temperature, Q(t) represents the influence of the external environment on the sample temperature, m` and C p represents the mass and specific heat capacity of the sample.

[0023] Furthermore, the reaction detection module uses an enzyme-linked immunosorbent assay to detect FHV monoclonal antibodies in cat serum samples to determine their presence and level. In the present invention, enzyme-linked immunosorbent assay (ELISA) is used as the main detection technology. Its principle is to use the specific binding of antibodies and antigens, and then use enzyme-labeled secondary antibodies or substrates to achieve quantitative or semi-quantitative detection of antibody-antigen conjugates. ELISA kits and standards are prepared, and reagents are diluted and prepared according to the operating instructions; the collected cat serum samples are processed, including centrifugal separation of serum, and appropriate dilutions are performed according to experimental requirements; the processed serum samples and standards are respectively added to the wells of the ELISA plate, and the reaction is incubated; the ELISA plate is washed to remove non-specific binders; according to the operating instructions provided by the kit, a substrate solution is used and the absorbance is measured by an enzyme reader to determine the presence and level of FHV monoclonal antibodies in the serum sample.

[0024] Furthermore, the diagnosis and judgment module determines whether the cat suffers from comorbid symptoms based on the test results obtained by the reaction detection module, and performs disease monitoring and treatment. The design of the diagnosis and judgment module needs to fully consider the clinical characteristics of comorbid symptoms and the test results of the reaction detection module, and further determine the severity of the disease.

[0025] Furthermore, the operation process of the diagnosis and judgment module includes the following steps:

[0026] (1) judging the presence and level of FHV monoclonal antibodies in the cat serum sample according to the test results obtained by the reaction detection module;

[0027] (2) Comprehensively assess whether the cat has comorbidities based on clinical symptoms and laboratory tests;

[0028] (3) Based on the evaluation results, determine the diagnosis of the cat patient, including confirmation, suspicion, or exclusion of comorbidities;

[0029] (4) Based on the diagnosis results, formulate corresponding treatment plans and monitoring strategies to monitor and treat the condition of cat patients.

[0030] Furthermore, the diagnostic judgment module monitors the immune indicators (x1, x2, ..., x n ) to form a column vector X as the input feature of the model: X = [x1, x2, ..., x n ] T , where X is the input feature column vector, x1, x2, ..., x n Respectively represent the first, second, and nth immune indicators, and define the weight matrix W1 from the input layer to the hidden layer:

[0031]

[0032] where w 11 represents the connection weight from the first neuron in the input layer to the first neuron in the hidden layer, w 12 represents the connection weight from the first neuron in the input layer to the second neuron in the hidden layer, w 1n represents the connection weight from the first neuron in the input layer to the nth neuron in the hidden layer, w 21 represents the connection weight from the second neuron in the input layer to the first neuron in the hidden layer, w 22 represents the connection weight from the second neuron in the input layer to the second neuron in the hidden layer, w 2n represents the connection weight from the second neuron in the input layer to the nth neuron in the hidden layer, w k1 represents the connection weight from the kth neuron in the input layer to the first neuron in the hidden layer, w k2 represents the connection weight from the kth neuron in the input layer to the second neuron in the hidden layer, w kn represents the connection weight from the kth neuron in the input layer to the nth neuron in the hidden layer, then the linear combination Z1 of the hidden layer input is expressed as:

[0033]

[0034] Define A1 as the activation value of the hidden layer and introduce nonlinear transformation: Define the weight matrix W2 from the hidden layer to the output layer:

[0035]

[0036] Among them, w 21 represents the connection weight from the second neuron in the hidden layer to the first neuron in the output layer, w 22 represents the connection weight from the second neuron in the hidden layer to the second neuron in the output layer, w 2k represents the connection weight from the second neuron in the hidden layer to the kth neuron in the output layer, w 31 represents the connection weight from the third neuron in the hidden layer to the first neuron in the output layer, w 32 represents the connection weight from the third neuron in the hidden layer to the second neuron in the output layer, w 3k represents the connection weight from the third neuron in the hidden layer to the kth neuron in the output layer, w m1 represents the connection weight from the hidden layer's neuron to the output layer's first neuron, w m2 represents the connection weight from the mth neuron in the hidden layer to the second neuron in the output layer, w mkrepresents the connection weight from the mth neuron in the hidden layer to the kth neuron in the output layer, and calculates the linear combination Z2 of the output layer input: Z2=W2·A1, represents the total input of the output layer neurons, and A2 is the activation value of the output layer, and we have:

[0037]

[0038] Define the weight vector W3 from the output layer to the final prediction as W3 = [w 31 , w 32 , ..., w 3m ] T , Z3=W3·A2 calculates the final predicted input linear combination Z3, then the final prediction result of the model is for:

[0039]

[0040] in, represents the probability that a cat has comorbidities, where b3 is the bias term, a 1×k matrix.

[0041] Furthermore, the verification experiment module verifies the effectiveness and feasibility of the monitoring system in clinical practice. The design and implementation of this module are important for evaluating the accuracy, sensitivity and specificity of the system. In the present invention, the function of the monitoring system is to assist in the diagnosis of comorbid symptoms. Therefore, the verification experiment module needs to focus on the accuracy and reliability of the system in the diagnosis of comorbid symptoms. At the same time, it is necessary to consider the sample source, sample quantity, and acquisition and recording of clinical information of clinical patients to ensure the feasibility and practicality of the verification experiment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The invention is further illustrated by the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0043] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] See also Figure 1 The present invention will be further described in conjunction with the following examples.

[0046] See also Figure 1 The present invention aims to provide a feline herpesvirus type 1 monitoring and evaluation platform and application, including an FHV antigen selection module, a monoclonal antibody preparation module, a serum sample collection module, a reaction detection module, a diagnosis and judgment module and a verification experiment module. The FHV antigen selection module selects FHV antigens suitable for immunizing mice and ensures that they can induce the production of efficient monoclonal antibodies. The monoclonal antibody preparation module obtains specific FHV monoclonal antibodies from immunized mice and ensures that they have high specificity and affinity. The serum sample collection module collects blood samples of cat patients as sample sources for subsequent experiments. The reaction detection module uses an enzyme-linked immunosorbent assay to detect FHV monoclonal antibodies in cat serum samples to determine their presence and level. The diagnosis and judgment module determines whether the cat suffers from comorbid symptoms based on the test results obtained by the reaction detection module, and performs disease monitoring and treatment. The verification experiment module verifies the effectiveness and feasibility of the monitoring system in clinical practice.

[0047] Specifically, the FHV antigen selection module selects FHV antigens suitable for immunizing mice and ensures that they can induce the production of highly efficient monoclonal antibodies.

[0048] Specifically, in order to select a suitable FHV antigen, the antigen comes from a natural isolate of FHV and a synthetic recombinant antigen, including proteins, peptides, and nucleic acids.

[0049] Specifically, the operation process of the FHV antigen selection module includes the following steps:

[0050] (1) Analyze and identify the species, subtypes, and pathogenic strains of FHV to determine the appropriate source of antigens;

[0051] (2) Designing an appropriate antigen expression system based on the characteristics of the selected antigen, including selecting a suitable expression vector and host cell;

[0052] (3) Express and purify the antigen to ensure the stability of its structure and activity;

[0053] (4) The specificity of the antigen is detected and verified through biochemical and immunological methods to ensure that it can effectively stimulate the immune mice to produce monoclonal antibodies.

[0054] Specifically, during the operation, considering the stability, purity and specificity of the FHV antigen, Western blot and ELISA techniques were selected to verify the specificity of the antigen to ensure that it met the expected requirements.

[0055] Specifically, the system monitors and evaluates the immune response of immunized mice to determine the immunogenicity of the antigen and the effect of the immune response.

[0056] Specifically, the monoclonal antibody preparation module obtains specific FHV monoclonal antibodies from immunized mice and ensures that the antibodies have high specificity and affinity.

[0057] Specifically, according to the specificity and immunogenicity of the FHV antigen, a suitable immune mouse strain is selected, and a suitable immunization scheme is designed to induce a specific antibody response.

[0058] Specifically, the strain of immunized mice is selected as the experimental animal strain, and BALB / c mice have good immune responsiveness and are easy to operate.

[0059] Specifically, design a suitable antigen immunization plan based on the weight and immunization scheme of the immunized mice, and ensure the stability and immunogenicity of the antigen

[0060] Specifically, FHV antigens are injected into immunized mice through appropriate routes to induce antigen-specific immune responses.

[0061] Specifically, after antigen immunization, spleen cells of the immunized mice are collected as raw materials for subsequent monoclonal antibody preparation.

[0062] Specifically, hybridoma cells are obtained by using cell fusion technology, and these hybridoma cells have stable monoclonal properties; the hybridoma cells are screened and identified, and hybridoma cell lines that produce FHV-specific monoclonal antibodies are screened, and then amplified and cultured.

[0063] Specifically, the serum sample collection module collects blood samples from cat patients as the sample source for subsequent experiments. The source of serum samples is clinical patients (cats). In the present invention, cat patients are used as the sample source, and blood samples of patients are collected for subsequent immunological testing and diagnosis.

[0064] Specifically, for the sample collection method, choose conventional blood collection techniques suitable for cats, including jugular vein blood collection and tail vein blood collection, to ensure that enough serum samples are collected for subsequent experiments. At the same time, attention should be paid to the operating specifications and sanitary conditions during blood collection to avoid contamination and sample loss.

[0065] Specifically, the operation process of the serum sample collection module includes: determining the clinical information and medical history of the cat patient, including age, gender, and disease course.

[0066] Specifically, choose the appropriate time and site for blood collection, including collecting blood when the cat patient is fasting or full, and choose a suitable site for blood collection.

[0067] Specifically, blood collection operations are performed, including the use of appropriate blood collection needles and tubes to avoid excessive blood drawing and sample contamination.

[0068] Specifically, the collected blood samples are placed in appropriate blood collection tubes and the sample information, including the cat patient number and blood collection time, is marked for subsequent experimental tracing and diagnosis.

[0069] Specifically, the serum sample collection module processes the collected samples and defines the matrix S to represent the evaluation of skill level and equipment performance, Q op Indicates the quality assessment of blood collection operation, including: Where T is the number of time points, M is the number of blood sampling sites, and the matrix S represents the skill level and equipment performance under different operating conditions. ij ∈S,S ij is the skill level and equipment performance of the jth blood collection site at the i-th time point. Then, the dynamic relationship between time and temperature during sample processing is established. The differential equation is used to represent the change of sample temperature T(t) after processing with time, and the influence of ambient temperature on sample temperature is considered. in, represents the change of sample temperature over time after treatment, represents the sample processing rate constant, t represents time, T 环境 Assume that the ambient temperature is the ambient temperature, the sample storage conditions are optimized, the optimal storage conditions are formulated, and the temperature and humidity factors are considered. Let C be the parameter set of the storage conditions:

[0070]

[0071] Among them, [t1, t2] is the time window, C represents the parameter set of the saving condition, T 目标 is the sample temperature, H(t) is the sample humidity, H 目标 The target sample humidity is the sample storage condition optimized by minimizing the objective function J(C). The optimal parameter set C is solved by the gradient descent method to minimize the objective function. The most suitable blood collection site is selected based on the physiological characteristics of cats. In order to be able to select the probability of a specific blood collection site for cat patients, the selection probability model is established as follows:

[0072]

[0073] Among them, P 选择 represents the probability of selecting a specific blood sampling site, I ip , I kq Both represent feature matrices, W jp , W jq Both represent weight matrices, α p , αq For I ip The regulatory factors and β p , β q W jp By evaluating the physiological characteristics of different parts, the most suitable blood collection site is selected. Since the blood collection rate during the collection process will affect the sample quality, a differential equation is used to describe the change of blood collection rate R(t) over time: Where R(t) represents the change of blood collection rate over time, and It is a parameter that controls the growth of the collection rate. For the risks and complications that may occur during the blood collection process, this system uses a probability model to evaluate the probability of risk events and calculate the expected risk value Risk:

[0074]

[0075] Among them, P i Indicates the probability of occurrence of various risk events, Severityi, Severity k It indicates the severity of various risk events, u and v are contribution factors. Due to the temperature changes of cat patients' blood samples during transportation, the temperature control of the sample transportation process is established. 运输 (t) Time-varying model:

[0076]

[0077] Where T 运输 (t) represents the change of sample temperature over time during transportation, T0 represents the initial temperature, Q(t) represents the influence of the external environment on the sample temperature, m` and C p represents the mass and specific heat capacity of the sample.

[0078] Specifically, the reaction detection module uses an enzyme-linked immunosorbent assay to detect FHV monoclonal antibodies in cat serum samples to determine their presence and levels.

[0079] Specifically, in the present invention, enzyme-linked immunosorbent assay (ELISA) is used as the main detection technology, the principle of which is to utilize the specific binding of antibody and antigen, and then use enzyme-labeled secondary antibody or substrate to achieve quantitative or semi-quantitative detection of antibody-antigen conjugates, prepare ELISA kits and standards, and dilute and prepare reagents according to the operating instructions.

[0080] Specifically, the collected cat serum samples are processed, including centrifugation to separate the serum and appropriate dilution according to experimental requirements.

[0081] Specifically, the treated serum samples and standards are added to the wells of the ELISA plate, respectively, and incubated for reaction.

[0082] Specifically, the ELISA plate is washed to remove non-specific binders.

[0083] Specifically, according to the operating instructions provided by the kit, a substrate solution is used and the absorbance is measured by an ELISA reader to determine the presence and level of FHV monoclonal antibodies in the serum sample.

[0084] Specifically, the diagnosis and judgment module determines whether the cat suffers from comorbid symptoms based on the test results obtained by the reaction detection module, and performs disease monitoring and treatment. The design of the diagnosis and judgment module needs to fully consider the clinical characteristics of comorbid symptoms and the test results of the reaction detection module.

[0085] Specifically, in the present invention, the presence and level of FHV monoclonal antibodies in cat serum samples are detected to assist in the diagnosis and monitoring of comorbid symptoms. Therefore, the diagnostic judgment module needs to judge whether the cat suffers from comorbid symptoms based on the test results, and further determine the severity of the disease.

[0086] Specifically, the operation process of the diagnosis and judgment module includes the following steps:

[0087] (1) judging the presence and level of FHV monoclonal antibodies in the cat serum sample according to the test results obtained by the reaction detection module;

[0088] (2) Comprehensively assess whether the cat has comorbidities based on clinical symptoms and laboratory tests;

[0089] (3) Based on the evaluation results, determine the diagnosis of the cat patient, including confirmation, suspicion, or exclusion of comorbidities;

[0090] (4) Based on the diagnosis results, formulate corresponding treatment plans and monitoring strategies to monitor and treat the condition of cat patients.

[0091] Specifically, the diagnostic judgment module monitors the immune indicators (x1, x2, ..., x n ) to form a column vector X as the input feature of the model: X = [x1, x2, ..., x n ] T , where X is the input feature column vector, x1, x2, ..., x n Respectively represent the first, second, and nth immune indicators, and define the weight matrix W1 from the input layer to the hidden layer:

[0092]

[0093] where w 11represents the connection weight from the first neuron in the input layer to the first neuron in the hidden layer, w 12 represents the connection weight from the first neuron in the input layer to the second neuron in the hidden layer, w 1n represents the connection weight from the first neuron in the input layer to the nth neuron in the hidden layer, w 21 represents the connection weight from the second neuron in the input layer to the first neuron in the hidden layer, w 22 represents the connection weight from the second neuron in the input layer to the second neuron in the hidden layer, w 2n represents the connection weight from the second neuron in the input layer to the nth neuron in the hidden layer, w k1 represents the connection weight from the kth neuron in the input layer to the first neuron in the hidden layer, w k2 represents the connection weight from the kth neuron in the input layer to the second neuron in the hidden layer, w kn represents the connection weight from the kth neuron in the input layer to the nth neuron in the hidden layer, then the linear combination Z1 of the hidden layer input is expressed as:

[0094]

[0095] Define A1 as the activation value of the hidden layer and introduce nonlinear transformation: Define the weight matrix W2 from the hidden layer to the output layer:

[0096]

[0097] Among them, w 21 represents the connection weight from the second neuron in the hidden layer to the first neuron in the output layer, w 22 represents the connection weight from the second neuron in the hidden layer to the second neuron in the output layer, w 2k represents the connection weight from the second neuron in the hidden layer to the kth neuron in the output layer, w 31 represents the connection weight from the third neuron in the hidden layer to the first neuron in the output layer, w 32 represents the connection weight from the third neuron in the hidden layer to the second neuron in the output layer, w 3k represents the connection weight from the third neuron in the hidden layer to the kth neuron in the output layer, w m1 represents the connection weight from the hidden layer's neuron to the output layer's first neuron, w m2 represents the connection weight from the mth neuron in the hidden layer to the second neuron in the output layer, w mk represents the connection weight from the mth neuron in the hidden layer to the kth neuron in the output layer, and calculates the linear combination Z2 of the output layer input: Z2=W2·A1, represents the total input of the output layer neurons, and A2 is the activation value of the output layer, and we have:

[0098]

[0099] Define the weight vector W3 from the output layer to the final prediction as W3 = [w 31 , w 32 , ..., w 3m ] T , Z3=W3·A2 calculates the final predicted input linear combination Z3, then the final prediction result of the model is for:

[0100]

[0101] in, represents the probability that a cat has comorbidities, where b3 is the bias term, a 1×k matrix.

[0102] Specifically, the validation experiment module verifies the effectiveness and feasibility of the monitoring system in clinical practice. The design and implementation of this module are important for evaluating the accuracy, sensitivity, and specificity of the system.

[0103] Specifically, in the present invention, the function of the monitoring system is to assist in the diagnosis of comorbid symptoms, so the verification experiment module needs to focus mainly on the accuracy and reliability of the system in the diagnosis of comorbid symptoms.

[0104] Specifically, factors such as the sample source of clinical patients, sample quantity, and acquisition and recording of clinical information need to be considered to ensure the feasibility and practicality of the validation experiment.

[0105] Beneficial effects of the invention: The invention realizes the preparation and selection of monoclonal antibodies against feline herpes virus (FHV), and obtains FHV monoclonal antibodies with high specificity and affinity by rationally selecting antigens and carrying out appropriate immunization schemes. Compared with traditional antibody preparation methods, the innovation of the invention is that it provides a more accurate and reliable antibody source, which lays a solid foundation for subsequent disease diagnosis and monitoring. The invention combines FHV monoclonal antibodies to design and establish a complete diagnostic monitoring system, which includes serum sample collection, reaction detection, diagnostic judgment and verification experiment modules, realizes comprehensive diagnosis and monitoring of the virus, combines monoclonal antibodies with modern biotechnology, realizes accurate detection and monitoring of feline herpes virus-related diseases, and the serum sample of the invention is This collection module processes the collected samples, performs a quality assessment on the blood collection operation, and takes into account the influence of temperature indicators on the force majeure factors such as the sample and the environment, and the sample and the transportation process. The innovation is that the present invention proposes a selection probability model in this field. In this model, the characteristic matrix and the weight matrix will affect the probability of a specific blood collection site of the cat patient. The calculation of the contribution factor is based on experimental data and expert opinions to make the entire model robust; in the diagnosis and judgment module, unlike the traditional method, the result output by the diagnosis and judgment module is the probability that the cat has comorbid symptoms. The advantage of doing so is that it can increase the subjective judgment of professionals, can assist professionals in the process of diagnosing and treating cat patients, and reduce the investment of manpower, time, and cost. The present invention has a fast and accurate diagnosis and monitoring effect, detects FHV monoclonal antibodies in serum samples through immunological technology, realizes early diagnosis and monitoring of comorbid symptoms, provides timely and effective treatment and management for cat patients, and provides individualized, timely and effective treatment plans for cat patients, reducing the misdiagnosis rate and missed diagnosis rate during treatment.

[0106] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A feline herpesvirus type 1 monitoring and evaluation platform, characterized in that: It includes an FHV antigen selection module, a monoclonal antibody preparation module, a serum sample collection module, a reaction detection module, a diagnosis judgment module and a verification experiment module. The FHV antigen selection module selects FHV antigens suitable for immunizing mice and ensures that they can induce the production of highly efficient monoclonal antibodies. The monoclonal antibody preparation module obtains specific FHV monoclonal antibodies from immunized mice and ensures that they have high specificity and affinity. The serum sample collection module collects blood samples from cat patients as sample sources for subsequent experiments. The reaction detection module uses enzyme-linked immunosorbent assay to detect FHV monoclonal antibodies in cat serum samples to determine their presence and level. The diagnosis judgment module determines whether the cat has comorbid symptoms, including conjunctivitis, rhinitis, pharyngitis, laryngitis, oral ulcers, and eye inflammation, based on the test results obtained by the reaction detection module, and monitors and treats the disease. The verification experiment module verifies the effectiveness and feasibility of the monitoring system in clinical practice. The serum sample collection module processes the collected samples, establishes a dynamic relationship between time and temperature during the sample processing process, uses a differential equation to represent the change of the sample temperature T(t) after processing with time, and considers the influence of the ambient temperature on the sample temperature, which is: in, represents the change of sample temperature over time after treatment, represents the sample processing rate constant, t represents time, T 环境 Assume that the ambient temperature is the ambient temperature, the sample storage conditions are optimized, and the optimal storage conditions are formulated. Considering the temperature and humidity factors, let C be the parameter set of the storage conditions: Among them, [t1, t2] is the time window, C represents the parameter set of the saving condition, T 目标 is the sample temperature, H(t) is the sample humidity, H 目标 The target sample humidity is the sample storage condition optimized by minimizing the objective function J(C). The optimal parameter set C is solved by the gradient descent method to minimize the objective function. The most suitable blood collection site is selected based on the physiological characteristics of cats. In order to be able to select the probability of a specific blood collection site for cat patients, the selection probability model is established as follows: Among them, P 选择 represents the probability of selecting a specific blood sampling site, I ip , I kq Both represent feature matrices, W jp , W jq Both represent weight matrices, α p , α q For I ip The regulatory factors and β p , β q W jp By evaluating the physiological characteristics of different parts, the most suitable blood collection site is selected. Since the blood collection rate during the collection process will affect the sample quality, a differential equation is used to describe the change of blood collection rate R(t) over time: Where R(t) represents the change of blood collection rate over time, and It is a parameter that controls the growth of the collection rate. For the risks and complications that may occur during the blood collection process, this system uses a probability model to evaluate the probability of risk events and calculate the expected risk value Risk: Among them, P i Indicates the probability of occurrence of various risk events, Severity i 、Severity k It indicates the severity of various risk events, u and v are contribution factors. Due to the temperature changes of cat patients' blood samples during transportation, the temperature control of the sample transportation process is established. 运输 (t) Time-varying model: Where T 运输 (t) represents the change of sample temperature over time during transportation, T0 represents the initial temperature, Q(t) represents the influence of the external environment on the sample temperature, m` and C p represents the mass and specific heat capacity of the sample.

2. A feline herpesvirus type 1 monitoring and evaluation platform according to claim 1, characterized in that: The FHV antigen selection module selects FHV antigens suitable for immunizing mice and ensures that they can induce the production of highly efficient monoclonal antibodies. In order to select suitable FHV antigens, the antigens come from natural isolates of FHV and synthetic recombinant antigens.

3. A feline herpesvirus type 1 monitoring and evaluation platform according to claim 2, characterized in that, The operation process of the FHV antigen selection module includes the following steps: (1) Analyze and identify the species, subtypes, and pathogenic strains of FHV to determine the appropriate source of antigens; (2) Designing an appropriate antigen expression system based on the characteristics of the selected antigen, including selecting a suitable expression vector and host cell; (3) Express and purify the antigen to ensure the stability of its structure and activity; (4) The specificity of the antigen is detected and verified through biochemical and immunological methods to ensure that it can effectively stimulate the immune mice to produce monoclonal antibodies.

4. A feline herpesvirus type 1 monitoring and evaluation platform according to claim 1, characterized in that: The monoclonal antibody preparation module obtains specific FHV monoclonal antibodies from immunized mice and ensures that they have high specificity and affinity. According to the specificity and immunogenicity of the FHV antigen, a suitable immunized mouse strain is selected, and a suitable immunization scheme is designed to induce a specific antibody response. The immunized mouse strain selects BALB / c mice as the experimental animal strain, and BALB / c mice have good immunoreactivity and easy operation. According to the weight and immunization scheme of the immunized mice, a suitable antigen immunization plan is designed, and the stability and immunogenicity of the antigen are ensured. The immunized mice are injected with FHV antigens through appropriate routes to induce antigen-specific immune responses. After antigen immunization, the spleen cells of the immunized mice are collected as raw materials for subsequent monoclonal antibody preparation. Hybridoma cells are obtained using cell fusion technology. The hybridoma cells are screened and identified, and the hybridoma cell lines that produce FHV-specific monoclonal antibodies are screened, amplified and cultured.

5. A feline herpesvirus type 1 monitoring and evaluation platform according to claim 1, characterized in that: The serum sample collection module collects blood samples from cat patients as the sample source for subsequent experiments, and the source of serum samples is clinical patient cats.

6. A feline herpesvirus type 1 monitoring and evaluation platform according to claim 1, characterized in that: The reaction detection module uses an enzyme-linked immunosorbent assay to detect FHV antibodies in feline serum samples to determine their presence and levels.

7. A feline herpesvirus type 1 monitoring and evaluation platform according to claim 1, characterized in that: The diagnosis and judgment module determines whether the cat suffers from comorbid symptoms based on the test results obtained by the reaction detection module, and conducts disease monitoring and treatment.

8. A feline herpesvirus type 1 monitoring and evaluation platform according to claim 7, characterized in that: The operation process of the diagnosis and judgment module includes the following steps: (1) judging the presence and level of FHV monoclonal antibodies in the cat serum sample according to the test results obtained by the reaction detection module; (2) Comprehensively assess whether the cat has comorbidities based on clinical symptoms and laboratory tests; (3) Based on the evaluation results, determine the diagnosis of the cat patient, including confirmation, suspicion, or exclusion of comorbidities; (4) Based on the diagnosis results, formulate corresponding treatment plans and monitoring strategies to monitor and treat the condition of cat patients.

9. A feline herpesvirus type 1 monitoring and evaluation platform according to claim 6, characterized in that: The diagnostic judgment module monitors the immune indicators (x1, x2, ..., x n ) form a column vector X as the input feature of the model: X = [x1, x2, ..., x n ] T , where X is the input feature column vector, x1,x2,…,x n Respectively represent the first, second, and nth immune indicators, and define the weight matrix W1 from the input layer to the hidden layer: where w 11 represents the connection weight from the first neuron in the input layer to the first neuron in the hidden layer, w 12 represents the connection weight from the first neuron in the input layer to the second neuron in the hidden layer, w 1n represents the connection weight from the first neuron in the input layer to the nth neuron in the hidden layer, w 21 represents the connection weight from the second neuron in the input layer to the first neuron in the hidden layer, w 22 represents the connection weight from the second neuron in the input layer to the second neuron in the hidden layer, w 2n represents the connection weight from the second neuron in the input layer to the nth neuron in the hidden layer, w k1 represents the connection weight from the kth neuron in the input layer to the first neuron in the hidden layer, w k2 represents the connection weight from the kth neuron in the input layer to the second neuron in the hidden layer, w kn represents the connection weight from the kth neuron in the input layer to the nth neuron in the hidden layer, then the linear combination Z1 of the hidden layer input is expressed as: Define A1 as the activation value of the hidden layer and introduce nonlinear transformation: Define the weight matrix W2 from the hidden layer to the output layer: Among them, w 21 represents the connection weight from the second neuron in the hidden layer to the first neuron in the output layer, w 22 represents the connection weight from the second neuron in the hidden layer to the second neuron in the output layer, w 2k represents the connection weight from the second neuron in the hidden layer to the kth neuron in the output layer, w 31 represents the connection weight from the third neuron in the hidden layer to the first neuron in the output layer, w 32 represents the connection weight from the third neuron in the hidden layer to the second neuron in the output layer, w 3k represents the connection weight from the third neuron in the hidden layer to the kth neuron in the output layer, w m1 represents the connection weight from the hidden layer's neuron to the output layer's first neuron, w m2 represents the connection weight from the mth neuron in the hidden layer to the second neuron in the output layer, w mk represents the connection weight from the mth neuron in the hidden layer to the kth neuron in the output layer, and calculates the linear combination Z2 of the output layer input: Z2=W2·A1, represents the total input of the output layer neurons, and A2 is the activation value of the output layer, and we have: Define the weight vector W3 from the output layer to the final prediction as W3 = [w 31 ,w 32 ,…,w 3m ] T , Z3=W3·A2 calculates the final predicted input linear combination Z3, then the final prediction result of the model is for: in, represents the probability that a cat has comorbidities, where b3 is the bias term, a 1×k matrix.

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