CN117169501B - Dynamic detection system for microorganisms on surface of surgical instrument - Google Patents
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Abstract
The invention discloses a dynamic detection system for microorganisms on the surface of a surgical instrument, which belongs to the field of microorganism detection of the medical instrument and comprises a sample acquisition module, a sample pretreatment module, an immunoreaction module, a fluorescence labeling module, a fluorescence detection module, a data acquisition and storage module, a data processing module and a pre-judging module, wherein the sample acquisition module comprises a sterile swab and a sampling cotton swab, and the edge part of the surgical instrument or a part which is easy to contact with body fluid of a patient is gently wiped. The dynamic detection system for the microorganisms on the surface of the surgical instrument can realize sample collection, sample pretreatment, microorganism number type identification, microorganism propagation prediction and prejudgment of the use of the surgical instrument, is beneficial to reducing the infection risk of a postoperative patient, prejudges whether the surgical instrument meets the operation requirement or not, and optimizes the operation flow and resource utilization so as to improve the operation efficiency.
Description
Technical Field
The invention belongs to the field of microorganism detection of medical instruments, and particularly relates to a dynamic microorganism detection system for the surface of a surgical instrument.
Background
Surgical instruments refer to tools and devices for performing surgical or medical procedures, having a variety of shapes, functions and uses, and being used in surgical procedures such as opening, suturing, hemostasis, sampling, visualization, detection, repair, etc., such as scalpels, scissors, suture needles, etc., which tend to make intimate contact with the body fluids of a patient during a surgical procedure;
the surgical instrument needs to be disinfected before the operation, although the disinfection program is strictly executed, the generated disinfection effect may not be 100%, some microorganisms still survive on the surface of the instrument, the propagation condition of the microorganisms on the surface of the surgical instrument cannot be predicted in the prior art, whether the surgical instrument meets the requirement of the whole field of operation is difficult to judge before the operation, and the infection risk of a postoperative patient is increased;
aiming at the above, the scheme provides a dynamic detection system for microorganisms on the surface of a surgical instrument so as to solve the technical problems.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides a dynamic detection system for microorganisms on the surface of a surgical instrument, which solves the technical problems by improving a pre-judging mode and a microorganism detection mode.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a dynamic detection system for microorganisms on the surface of a surgical instrument comprises a sample acquisition module, a sample pretreatment module, an immunoreaction module, a fluorescence labeling module, a fluorescence detection module, a data acquisition and storage module, a data processing module and a pre-judging module;
the sample collection module comprises a sterile swab and a sampling cotton swab, and the blade part of the surgical instrument or a part which is easy to contact with body fluid of a patient is gently wiped, so that the sufficient surface area is ensured to be contacted, and a sufficient microorganism sample can be obtained;
the sample pretreatment module comprises centrifugal operation and filtering operation, and is used for pretreating the microorganism sample collected by the sample collection module, extracting supernatant through centrifugation, removing certain impurities through filtration so as to extract microorganisms and remove possible interferents;
the immune reaction module is used for measuring the microorganism sample pretreated by the sample pretreatment module by an immunoassay method, so that the microorganism sample is combined with the specific antibody to generate immune reaction;
the fluorescent marking module is used for carrying out immune reaction on the secondary antibody with fluorescent marking and the specific antibody combined in the immune reaction module, so that the antigen on the surface of the microbial sample is fluorescently marked;
the fluorescence detection module comprises a fluorescence immunoassay analyzer and is used for detecting and identifying fluorescent signals in the microorganism sample marked by the fluorescence marking module, and measuring and analyzing the fluorescence intensity by exciting and collecting the fluorescent signals in the microorganism sample and converting the fluorescent signals into digital signals;
the data acquisition and storage module is used for collecting and sorting the data detected by the fluorescence detection module, collecting spectrum data in the microorganism sample through an infrared spectrometer, classifying and building a database by adopting a MySQL database according to specific time and types, and uploading the database to a cloud for backup storage;
the data processing module is used for processing the acquired microorganism sample data to extract useful information and detecting the existence, the quantity and the dynamic change of microorganisms;
the pre-judging module is used for judging whether the surgical instrument meets the requirement of the operation or not by integrating the information obtained by the data processing module and combining the environment of the operating room site and the duration time of the operation;
the data processing module is provided with a filtering algorithm, the collected microorganism sample data is subjected to preselection processing, the reliability of the data is further improved, and a specific algorithm formula is as follows:
；
wherein,as a result of the filtered data points,then it is the raw data point that,in order to filter the size of the window,representing a current data pointBy which the data points within the window can be averaged to obtain filtered data points.
The data processing module is also provided with a spectrum analysis method for extracting characteristics of microorganism sample data, and the specific operation steps are as follows:
firstly, dividing the data subjected to a filtering algorithm into a plurality of small segments, and carrying out spectrum analysis and feature extraction on each small segment;
the discrete Fourier transform is adopted to convert the time domain data into the frequency domain data, and a specific algorithm formula is as follows:
；
wherein,is the base of the natural logarithm,then it is an imaginary unit, in the above formulaRepresentation pairFrom 0 toGiven a length ofIs a discrete sequence of (a)WhereinDiscrete fourier transform converts the discrete sequence into a lengthComplex sequences of (2)WhereinThen the contribution of the original sequence over the components of different frequencies is represented;
and further calculates the spectral power spectral density according to the result of the discrete Fourier transformAnd extracting spectrum characteristics, wherein a specific algorithm formula is as follows:
；
wherein,expressed in frequency index asThe power spectral density over the component(s) of (c) is,as a result of the discrete fourier transform after the frequency domain transform,is the sequence length and can be further based onThe peak frequency in (a) determines the dominant frequency.
The data processing module is also provided with a microorganism identification judging system, which comprises the following specific steps:
firstly, collecting the spectrum characteristics of known microorganisms, constructing a microorganism database, and introducing the database into a microorganism identification judging system;
according to the spectrum characteristics of the microorganism sample extracted in the spectrum analysis method, combining a database of microorganisms, and adopting a Manhattan distance method to compare and match the spectrum characteristics of the microorganisms in the microorganism sample;
and determining the number of various microorganisms in the microorganism sample by combining the fluorescence intensity analysis in the fluorescence detection module.
The spectrum characteristics of the microorganisms in the microorganism samples are compared and matched by combining the spectrum characteristics of the microorganism samples extracted by the spectrum analysis method with a database of the microorganisms and adopting a Manhattan distance method, and a specific algorithm formula is as follows:
；
wherein,representing the dimension of the vector i.e. the number of features,andrespectively represent the sample to be identified and the database sample in the first placeAnd comparing the spectral feature vector A of the sample to be identified with the spectral feature vector B of each microbial sample in the database, and calculating the Manhattan distance between each microbial sample and the sample to be identified, wherein the smaller the Manhattan distance is, the more similar the spectral features of the microbial samples are, so that the microbial species in the microbial samples are determined.
An integration system is arranged in the pre-judging module, and the specific operation steps are as follows:
the method comprises the steps of inputting operation duration time and operation environment temperature into a pre-judging module in advance, integrating the types and numbers of microorganisms on the surgical instruments obtained by a data processing module by the pre-judging module, analyzing by combining known microorganism information data, pre-judging the proliferation condition of the microorganisms on the surgical instruments required to be used, obtaining the quantity change of the microorganisms by combining the operation environment temperature and the microorganism population quantity, and judging whether the operation requirement is met by setting a threshold value and combining the proliferation rate of the microorganisms, wherein the specific algorithm formula is as follows:
；
wherein,in order to achieve the rate of propagation of the microorganisms,representing the amount of change in the amount of microorganisms,representing the duration of the operation, whenWhen the set threshold value is exceeded, the risk of reproduction is higher;
further based on historical data of the number of microorganisms on the surgical instrument, the propagation trend of the microorganisms is predicted by a trend analysis method, and an exponential smoothing method is adopted, wherein a specific algorithm formula is as follows:
initial smoothed value:whereinFor the first observed number of microorganisms;
updating the smoothed value:wherein t is the time step, and the time step,then it is a smoothing coefficient；
Future value prediction:predicting a smoothed value of t+1;
and continuously updating and predicting the smooth value to obtain the trend of microorganism propagation in a period of time in the future, and judging whether the operation requirement is met or not by combining the propagation rate.
Further, the sample pretreatment module can pretreat the microorganism sample collected by the sample collection module, thereby facilitating the smooth operation of the follow-up immune reaction module, and the specific operation steps are as follows:
the sterile swab and the sampling cotton swab are placed in normal saline, so that the microorganism sample collected by the sterile swab and the sampling cotton swab is fully released into the normal saline, the supernatant is separated by the centrifugal machine, and then the supernatant is filtered, and larger solid particles are filtered out, so that a purer microorganism sample is obtained.
Further, the immune response module can determine the microorganism sample pretreated by the sample pretreatment module through an immunoassay method, wherein the immunoassay method is an immunochromatography method, and the specific operation steps are as follows:
selecting a proper immunochromatographic test paper;
adding a proper amount of buffer solution into the microorganism sample pretreated by the sample pretreatment module for dilution, and applying the buffer solution containing the microorganism sample to immunochromatography test paper;
introducing active chemical groups on the carrier, so that the carrier can be covalently combined with amino acid residues on the antibody, and further fixing the specific antibody solid-phase probe on the carrier;
at specific locations in the immunochromatographic material, microorganisms in the microorganism sample will bind to the solid-phase probes, eventually forming specific antigen-antibody complexes.
Furthermore, the fluorescent labeling module performs immune reaction with the combined specific antibody in the immune reaction module through the secondary antibody with fluorescent label, so that the antigen on the surface of the microbial sample is fluorescently labeled, and the specific operation steps are as follows:
selecting an adaptive secondary antibody according to the type of the specific antibody solid-phase probe used in the immune reaction module;
selecting a proper fluorescent probe or a labeled antibody according to the type of the selected secondary antibody, wherein the specific fluorescent probe is fluorescent protein;
chemically conjugating the secondary antibody with the fluorescent substance to enable the fluorescent substance to form conjugated complex with the secondary antibody;
mixing the labeled fluorescent secondary antibody with a microbial sample to be detected to form an immunoreaction mixture, and properly diluting according to the concentration of the marker;
the immunoreaction mixture is subjected to immunoreaction under proper conditions, so that the labeled fluorescent secondary antibody is specifically combined with the target microorganism.
Further, the secondary antibody is chemically conjugated with the fluorescent substance to form a conjugated compound with the fluorescent substance, and the method adopted by the method is a chemical coupling method, and comprises the following specific operation steps:
activating the fluorescent protein with an appropriate activator according to its chemical properties;
reacting the secondary antibody with an active chemical group to enable the secondary antibody to have reactivity of chemical coupling with fluorescent protein;
mixing the activated fluorescent protein and the activated secondary antibody, and performing chemical coupling reaction under proper conditions, wherein the chemical coupling reaction comprises the reaction of an amino group and isocyanate, and the reaction of a sulfhydryl group and maleimide;
the chemically coupled product was purified to remove impurities and the product was further observed and detected using a spectrophotometer.
Compared with the prior art, the invention has the beneficial effects that:
1. in the invention, the secondary antibody is marked by adopting fluorescent protein, and then the secondary antibody performs immune reaction with a microorganism sample to be detected, so that the microorganism can be quantitatively measured in a fluorescent detection module, and medical staff can know the number of microorganisms on the surface of the surgical instrument, thereby helping to guide infection control measures and the use of the surgical instrument;
2. in the invention, the fluorescent protein labeled secondary antibody is adopted, and has high sensitivity and specificity, so that microorganisms can be effectively detected and identified, and different types and numbers of microorganisms can be distinguished by the method, thereby being beneficial to providing accurate microorganism number information;
3. compared with the traditional microorganism detection method, the method provided by the invention has the advantages that the rapid detection and analysis can be realized by adopting the fluorescent protein labeled secondary antibody for immune reaction, the method is simple to operate, the complex culture step is not needed, and the time and labor cost are saved;
4. in the invention, the data of the sample to be detected is collected and collected through the data collection and storage module, a database is built and the cloud end is uploaded, the data can be used as backup for subsequent backtracking and examination, then the data is transmitted to the data processing module, the data is preprocessed through the filtering algorithm, the data quality and the reliability are improved, and an accurate data base is provided for subsequent analysis and comparison;
5. according to the invention, the spectrum data of the microbial sample to be detected is obtained through Fourier transformation, the spectrum data can reflect the characteristics and differences of the microbial sample, a reference basis is provided for subsequent microbial species identification, and accurate microbial species information is obtained through Manhattan distance method comparison with the spectrum data of known microbes;
6. according to the invention, the information is integrated through the pre-judging module, the microbial quantity variation can be obtained according to the detection result of the microbes on the surface of the surgical instrument, the operation duration time and the operation environment temperature through analysis and calculation of the pre-judging module, and compared with the threshold value, when the microbial quantity variation on the surface of the surgical instrument exceeds the set threshold value, the propagation risk is higher, and medical staff is prompted to reconsider the use of the surgical instrument so as to reduce the infection risk;
7. in the invention, the pre-judging module can also analyze the use condition of the surgical instrument and the rule of microorganism dynamic change, and the operation flow and resource utilization can be optimized by predicting the requirement of the surgical instrument, and the use sequence of the surgical instrument can be adjusted according to the result of the pre-judging module so as to improve the operation efficiency;
8. in the invention, a trend analysis method is also arranged in the pre-judging module, and according to the historical data of the number of microorganisms on the surgical instrument, the trend analysis and the prediction are carried out by using an exponential smoothing method, so that the trend of microorganism propagation in a period of time in the future can be predicted, thereby being beneficial to medical staff to take corresponding preventive and control measures on the prediction result, reducing the risk of related infection of the surgery and protecting the health of patients.
The whole surgical instrument surface microorganism dynamic detection system can realize sample collection, sample pretreatment, microorganism number type identification, microorganism propagation prediction and surgical instrument use pre-judgment, is beneficial to reducing the infection risk of postoperative patients, pre-judges whether the surgical instrument meets the operation requirement or not, and optimizes the operation flow and resource utilization so as to improve the operation efficiency.
Drawings
FIG. 1 is a block diagram of a dynamic detection system for microorganisms on the surface of a surgical instrument according to the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in FIG. 1, the dynamic detection system for microorganisms on the surface of a surgical instrument comprises a sample acquisition module, a sample pretreatment module, an immune response module, a fluorescence labeling module, a fluorescence detection module, a data acquisition and storage module, a data processing module and a pre-judging module;
the sample collection module comprises a sterile swab and a sampling cotton swab, and the blade part of the surgical instrument or a part which is easy to contact with body fluid of a patient is gently wiped, so that enough surface area is ensured to be contacted to obtain enough samples;
it should be noted that, the aseptic swab and the sampling swab can ensure that the collected microorganism sample is pure, and in the wiping process of the blade part of the surgical instrument or the part which is easy to contact with the body fluid of the patient, certain control is required to be performed on the force, and the sufficient wiping time is ensured, so that the sufficient microorganism sample is ensured to be dipped.
The sample pretreatment module comprises a centrifugation operation and a filtration operation, and is used for pretreating the microorganism sample collected by the sample collection module so as to extract microorganisms and remove possible interference objects;
the sample pretreatment module can pretreat the microorganism sample collected by the sample collection module so as to facilitate the smooth operation of the follow-up immune reaction module, and the specific steps are as follows:
placing the sterile swab and the sampling cotton swab in normal saline, fully releasing the microorganism sample collected by the sterile swab and the sampling cotton swab into the normal saline, centrifuging to separate supernatant, filtering the supernatant, and filtering out larger solid particles to obtain a purer microorganism sample;
it should be noted that, the sterile swab and the sampling cotton swab need to be placed in a sterile container, and then sufficient physiological saline is added, and the physiological saline needs to completely submerge the sterile swab and the sampling cotton swab to enable the sterile swab and the sampling cotton swab to be fully soaked.
The immune reaction module is used for measuring the microorganism sample pretreated by the sample pretreatment module by an immunoassay method, so that the microorganism sample is combined with the specific antibody to generate immune reaction;
the immune reaction module can measure the microorganism sample pretreated by the sample pretreatment module through an immunoassay method, wherein the immunoassay method is an immunochromatography method, and the specific operation steps are as follows:
firstly, selecting proper immunochromatographic test paper;
adding a proper amount of buffer solution into the microorganism sample pretreated by the sample pretreatment module for dilution, and applying the buffer solution containing the microorganism sample to immunochromatography test paper;
introducing active chemical groups on the carrier to enable the active chemical groups to be covalently combined with amino acid residues on the antibody, so that the specific antibody solid-phase probe is fixed on the carrier;
at a specific position in the immunochromatography material, microorganisms in a microorganism sample can be combined with a solid-phase probe to finally form a specific antigen-antibody complex;
by using immunochromatography, a microorganism in a sample can be combined with a specific antibody to obtain an antigen-antibody complex, and after the immunochromatography is finished, a washing operation is required to wash out the specific antibody which is not combined with the microorganism, so that the combination with a subsequent fluorescent secondary antibody is prevented, errors and deviations of data are caused, and the whole flow is influenced.
The fluorescent marking module is used for carrying out immune reaction on the secondary antibody with fluorescent marking and the specific antibody combined in the immune reaction module, so that the antigen on the surface of the microbial sample is fluorescently marked;
the fluorescent labeling module generates immune reaction with the combined specific antibody in the immune reaction module through the secondary antibody with fluorescent label, so that the antigen on the surface of the microbial sample is fluorescently labeled, and the specific operation steps are as follows:
selecting a corresponding secondary antibody according to the type of the specific antibody solid-phase probe used in the immune reaction module;
selecting a proper fluorescent probe or a labeled antibody according to the type of the selected secondary antibody, wherein the specific fluorescent probe is fluorescent protein;
chemically conjugating the secondary antibody with the fluorescent substance to form a conjugated compound with the fluorescent substance;
mixing the labeled fluorescent secondary antibody with a microbial sample to be detected to form an immunoreaction mixture, and properly diluting according to the concentration of the marker;
performing immune reaction on the immune reaction mixture under proper conditions to ensure that the labeled fluorescent secondary antibody is specifically combined with target microorganisms;
the secondary antibody and the fluorescent substance are subjected to chemical conjugation to form a conjugated compound, and the method adopted by the method is a chemical coupling method, and comprises the following specific operation steps:
activating the fluorescent protein with an appropriate activator according to its chemical properties;
reacting the secondary antibody with an active chemical group to enable the secondary antibody to have reactivity of chemical coupling with fluorescent protein;
mixing the activated fluorescent protein and the activated secondary antibody, and performing chemical coupling reaction under proper conditions, wherein the chemical coupling reaction comprises the reaction of an amino group and isocyanate, and the reaction of a sulfhydryl group and maleimide;
purifying the chemical coupling product, removing impurities, and further observing and detecting the product by using a spectrophotometer;
after the fluorescent protein labeled secondary antibody reacts with the microbial sample, the microbial sample needs to be washed to remove the secondary antibody which is not combined with the microbial sample, so that only the antigen combined with the microorganism is fluorescently labeled, and the influence of the unbound fluorescent protein secondary antibody on the subsequent detection result is prevented, thereby leading to inaccurate prediction result.
The fluorescence detection module comprises a fluorescence immunoassay analyzer and is used for detecting and identifying fluorescent signals in the microorganism sample marked by the fluorescence marking module, and measuring and analyzing the fluorescence intensity by exciting and collecting the fluorescent signals in the microorganism sample and converting the fluorescent signals into digital signals;
after obtaining the fluorescence-labeled microorganism sample, the microorganism sample is placed in a fluorescence immunoassay analyzer, so that the fluorescence intensity inside the sample can be measured and analyzed, the information of the microorganisms inside the sample can be obtained, the information is converted into a digital signal and transmitted to a next module, the number of the microorganisms is determined, and the number of the microorganisms on the surgical instrument is estimated by combining the sampled area.
The data acquisition and storage module is used for collecting and sorting the data detected by the fluorescence detection module, collecting spectrum data in the microorganism sample through an infrared spectrometer, classifying and building a database by adopting a MySQL database according to specific time and types, and uploading the database to a cloud for backup storage;
it should be noted that, collect the spectrum data of microorganism sample through infrared spectrometer to relate with the quantity of microorganism, unified integration back build the storehouse and store and upload to the high in the clouds, prevent to meet special circumstances and lead to data loss, after uploading to the high in the clouds, with data transmission to next module carry out data processing.
The data processing module is used for processing the acquired microorganism sample data to extract useful information and detecting the existence, the quantity and the dynamic change of microorganisms;
the data processing module is provided with a filtering algorithm, the collected microorganism sample data is subjected to preselection processing, the reliability of the data is further improved, and a specific algorithm formula is as follows:
；
wherein,is the data point after the filtering and,is the original data point of the data,is the size of the filter window and,representing a current data pointThe average calculation is carried out on the data points in the window through the method, so that filtered data points are obtained;
it should be noted that, through the filtering algorithm, the signals obtained in the data acquisition and storage module can be limited within a specific frequency range, so that the subsequent spectrum analysis is facilitated, and through the filtering, the components with specific frequencies can be selectively passed or suppressed, so that valuable frequency information is extracted, and the subsequent confirmation of the microorganism types is facilitated.
The data processing module is also provided with a spectrum analysis method for extracting characteristics of microorganism sample data, and the specific operation steps are as follows:
firstly, dividing the data subjected to a filtering algorithm into a plurality of small segments, and carrying out spectrum analysis and feature extraction on each small segment;
the discrete Fourier transform is adopted to convert the time domain data into the frequency domain data, and the algorithm formula is as follows:
；
wherein,is the base of the natural logarithm,in imaginary units, in the above formulaRepresentation pairSumming from 0 to N-1, given a length ofIs a discrete sequence of (a)WhereinDiscrete fourier transform converts the discrete sequence into a lengthComplex sequences of (2)WhereinThen the contribution of the original sequence over the components of different frequencies is represented;
and then calculating the frequency spectrum power spectral density according to the result of the discrete Fourier transformAnd extracting spectrum characteristics, wherein a specific algorithm formula is as follows:
；
wherein,expressed in frequency index asThe power spectral density over the component(s) of (c) is,as a result of the discrete fourier transform after the frequency domain transform,is the sequence length and can be further based onThe peak frequency of the (b) is used for determining a main frequency;
the data processing module is also provided with a microorganism identification judging system, which comprises the following specific steps:
firstly, collecting the spectrum characteristics of known microorganisms, constructing a microorganism database, and introducing the database into a microorganism identification judging system;
according to the spectrum characteristics of the microorganism sample extracted in the spectrum analysis method, combining a database of microorganisms, and adopting a Manhattan distance method to compare and match the spectrum characteristics of the microorganisms in the microorganism sample;
determining the number of various microorganisms in the microorganism sample by combining with the fluorescence intensity analysis in the fluorescence detection module;
according to the spectrum characteristics of the microorganism sample extracted in the spectrum analysis method, combining with a database of microorganisms, adopting a Manhattan distance method to compare and match the spectrum characteristics of the microorganisms in the microorganism sample, wherein the algorithm formula is as follows:
；
wherein,representing the dimension of the vector i.e. the number of features,andrespectively represent the sample to be identified and the database sample in the first placeComparing the spectral feature vector A of the sample to be identified with the spectral feature vector B of each microbial sample in the database, and calculating the Manhattan distance between each microbial sample and the sample to be identified, wherein the smaller the Manhattan distance is, the more similar the spectral features of the microbial samples are;
it should be noted that, firstly, a spectrum analysis method is adopted to perform spectrum analysis on the data subjected to the filtering algorithm, firstly, discrete fourier transformation is used to further convert time domain data into frequency domain data, frequency domain features are extracted, the frequency domain features are compared with those in a known microorganism database, a manhattan distance method is adopted to determine specific microorganism types, the manhattan distance measures the sum of absolute values of differences of two vectors in each dimension, namely, the smaller the manhattan distance between the spectrum feature vector of a sample to be identified and the spectrum feature vector of a microorganism sample in the database is, the more similar the spectrum features of the two are represented, and therefore the microorganism types of the sample to be identified are determined.
The pre-judging module is used for judging whether the surgical instrument meets the requirement of the operation or not by integrating the information obtained by the data processing module and combining the environment of the operating room site and the duration time of the operation;
an integration system is arranged in the pre-judging module, and the specific operation steps are as follows:
the method comprises the steps of inputting operation duration time and operation environment temperature into a pre-judging module in advance, integrating the types and numbers of microorganisms on the surgical instruments obtained by a data processing module by the pre-judging module, analyzing by combining known microorganism information data, pre-judging the proliferation condition of the microorganisms on the surgical instruments required to be used, obtaining the quantity change of the microorganisms by combining the operation environment temperature and the microorganism population quantity, and judging whether the operation requirement is met by setting a threshold value and combining the proliferation rate of the microorganisms, wherein the specific algorithm formula is as follows:
；
wherein,in order to achieve the rate of propagation of the microorganisms,representing the amount of change in the amount of microorganisms,representing the duration of the operation, whenWhen the set threshold value is exceeded, the propagation risk is representedHigh;
further based on historical data of the number of microorganisms on the surgical instrument, the propagation trend of the microorganisms is predicted by a trend analysis method, and an exponential smoothing method is adopted, wherein a specific algorithm formula is as follows:
initial smoothed value:whereinFor the first observed number of microorganisms;
updating the smoothed value:wherein t is the time step, and the time step,then it is a smoothing coefficient；
Future value prediction:predicting a smoothed value of t+1;
continuously updating and predicting the smooth value to obtain a microbial propagation trend in a future period of time, and judging whether the microbial propagation trend meets the operation requirement or not by combining the propagation rate;
it should be noted that, through the pre-judging module, it can be quickly judged whether the measured surgical instrument can meet the standard specification of the surgery to be performed, in the exponential smoothing method, the largerWill make the new data have a greater impact on the smoothed value and a smaller impactThe influence of the historical data on the smoothed value is larger, so that in the actual application process, proper smoothing is selected according to the actual requirementsCoefficients ofDetermination of the best by trial and verificationValue to further improve accuracy and reliability of the prediction.
In summary, by means of the above technical scheme of the present invention, the surface of a surgical instrument to be operated is gently wiped by a sample collection module, a sterile swab and a sampling cotton swab, enough microorganism samples are collected, then the microorganisms are extracted and solid particle impurities are removed by centrifugation and filtration operation of a sample pretreatment module, then the extracted microorganism samples are subjected to immune reaction by an immune reaction module and a fluorescent labeling module, antigen-antibody complex is produced by adopting specific antibodies, then a proper secondary antibody is selected and combined with the secondary antibody according to the kind of the specific antibodies used, and microorganisms in the samples are labeled by immune reaction of the fluorescent secondary antibody and the antigen-antibody complex;
the fluorescence immunoassay analyzer in the fluorescence acquisition module is used for measuring, integrating and analyzing the intensity of fluorescence, outputting a digital signal to enter the data acquisition and storage module, collecting spectrum data in a microorganism sample through the infrared spectrometer, and combining the data uploaded by the fluorescence acquisition module, and the whole database is built and uploaded to a cloud for storage backup, so that the subsequent review is facilitated;
the method comprises the steps of preprocessing obtained data through a data processing module by a filtering algorithm, improving the quality and the credibility of the data, extracting spectral features through discrete Fourier transformation, and identifying the types of microorganisms through a microorganism identification judging system in the data processing module, combining the spectral features of known microorganisms and comparing through a Manhattan distance method;
through the pre-judging module, the information of all the modules is integrated, the duration time and the operation environment of the operation are combined, the trend analysis method is adopted to predict microorganisms on the surface of the surgical instrument, whether the surgical instrument meets the requirement of the field operation is finally judged, and then medical staff is helped to optimize the operation flow and the resource utilization, the risk of postoperative patient infection is reduced, and the operation efficiency is improved.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented; the modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of this embodiment.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (5)
1. A dynamic detection system for microorganisms on the surface of a surgical instrument, which is characterized in that: the device comprises a sample acquisition module, a sample preprocessing module, an immune reaction module, a fluorescence labeling module, a fluorescence detection module, a data acquisition and storage module, a data processing module and a pre-judging module;
the sample collection module comprises a sterile swab and a sampling cotton swab, and the blade part of the surgical instrument or the part which is easy to contact with body fluid of a patient is gently wiped, so that enough surface area is ensured to be contacted to obtain enough samples;
the sample pretreatment module comprises centrifugal operation and filtering operation, and is used for pretreating the microorganism sample collected by the sample collection module so as to extract microorganisms and remove possible interference objects;
the immune reaction module is used for measuring the microorganism sample pretreated by the sample pretreatment module by an immunoassay method, so that the microorganism sample is combined with the specific antibody to generate immune reaction;
the fluorescent marking module is used for carrying out immune reaction on the secondary antibody with fluorescent marking and the specific antibody combined in the immune reaction module, so that the antigen on the surface of the microbial sample is fluorescently marked;
the fluorescence detection module comprises a fluorescence immunoassay analyzer and is used for detecting and identifying fluorescent signals in the microorganism sample marked by the fluorescence marking module, and measuring and analyzing the fluorescence intensity by exciting and collecting the fluorescent signals in the microorganism sample and converting the fluorescent signals into digital signals;
the data acquisition and storage module is used for collecting and sorting the data detected by the fluorescence detection module, collecting spectrum data in the microorganism sample through an infrared spectrometer, classifying and building a database by adopting a MySQL database according to specific time and types, and uploading the database to a cloud for backup storage;
the data processing module is used for processing the acquired microorganism sample data to extract useful information and detecting the existence, the quantity and the dynamic change of microorganisms;
the pre-judging module is used for judging whether the surgical instrument meets the requirement of the operation or not by integrating the information obtained by the data processing module and combining the environment of the operating room site and the duration time of the operation;
the data processing module is provided with a filtering algorithm, the collected microorganism sample data is subjected to preselection processing, the reliability of the data is further improved, and a specific algorithm formula is as follows:
；
wherein,is a filtered numberData point->Is the original data point, N is the size of the filter window, k represents the current data point +.>The average calculation is carried out on the data points in the window through the method, so that filtered data points are obtained;
the data processing module is also provided with a spectrum analysis method for extracting characteristics of microorganism sample data, and the specific operation steps are as follows:
firstly, dividing the data subjected to a filtering algorithm into a plurality of small segments, and carrying out spectrum analysis and feature extraction on each small segment;
the discrete Fourier transform is adopted to convert the time domain data into the frequency domain data, and the algorithm formula is as follows:
；
wherein,is the base of the natural logarithm,in imaginary units, in the above formulaRepresentation pairFrom 0 toGiven a length ofIs a discrete sequence of (a)WhereinDiscrete fourier transform converts the discrete sequence into a lengthComplex sequences of (2)WhereinThen the contribution of the original sequence over the components of different frequencies is represented;
and then calculating the frequency spectrum power spectral density according to the result of the discrete Fourier transformAnd extracting spectrum characteristics, wherein a specific algorithm formula is as follows:
；
wherein,expressed in frequency index asThe power spectral density over the component(s) of (c) is,as a result of the discrete fourier transform after the frequency domain transform,is the sequence length and can be further based onThe peak frequency of the (b) is used for determining a main frequency;
the data processing module is also provided with a microorganism identification judging system, which comprises the following specific steps:
firstly, collecting the spectrum characteristics of known microorganisms, constructing a microorganism database, and introducing the database into a microorganism identification judging system;
according to the spectrum characteristics of the microorganism sample extracted in the spectrum analysis method, combining a database of microorganisms, and adopting a Manhattan distance method to compare and match the spectrum characteristics of the microorganisms in the microorganism sample;
determining the number of various microorganisms in the microorganism sample by combining with the fluorescence intensity analysis in the fluorescence detection module;
the spectrum characteristics of the microorganisms in the microorganism samples are compared and matched by combining the spectrum characteristics of the microorganism samples extracted by the spectrum analysis method with a database of the microorganisms and adopting a Manhattan distance method, and the algorithm formula is as follows:
；
wherein,representing the dimension of the vector i.e. the number of features,andrespectively represent the sample to be identified and the database sample in the first placeThe value on each feature is used for comparing the spectrum feature vector A of the sample to be identified with the spectrum feature vector B of each microbial sample in the database, and the Manhattan distance between each microbial sample and the sample to be identified is calculated, wherein the smaller the Manhattan distance isThe more similar they represent spectral features;
an integration system is arranged in the pre-judging module, and the specific operation steps are as follows:
the method comprises the steps of inputting operation duration time and operation environment temperature into a pre-judging module in advance, integrating the types and numbers of microorganisms on the surgical instruments obtained by a data processing module by the pre-judging module, analyzing by combining known microorganism information data, pre-judging the proliferation condition of the microorganisms on the surgical instruments required to be used, obtaining the quantity change of the microorganisms by combining the operation environment temperature and the microorganism population quantity, and judging whether the operation requirement is met by setting a threshold value and combining the proliferation rate of the microorganisms, wherein the specific algorithm formula is as follows:
；
wherein,in order to achieve the rate of propagation of the microorganisms,representing the amount of change in the amount of microorganisms,representing the duration of the operation, whenWhen the set threshold value is exceeded, the risk of reproduction is higher;
further based on historical data of the number of microorganisms on the surgical instrument, the propagation trend of the microorganisms is predicted by a trend analysis method, and an exponential smoothing method is adopted, wherein a specific algorithm formula is as follows:
initial smoothed value:whereinFor the first observed number of microorganisms;
updating the smoothed value:wherein t is the time step, and the time step,then it is a smoothing coefficient；
Future value prediction:for the followingPredicting the smoothed value of (2);
and continuously updating and predicting the smooth value to obtain the trend of microorganism propagation in a period of time in the future, and judging whether the operation requirement is met or not by combining the propagation rate.
2. A surgical instrument surface microorganism dynamic detection system according to claim 1, wherein: the sample pretreatment module can pretreat the microorganism sample collected by the sample collection module so as to facilitate the smooth operation of the follow-up immune reaction module, and the specific steps are as follows:
placing the sterile swab and the sampling cotton swab in normal saline, fully releasing the microorganism sample collected by the sterile swab and the sampling cotton swab into the normal saline, centrifuging to separate supernatant, filtering the supernatant, and filtering out larger solid particles to obtain a purer microorganism sample.
3. A surgical instrument surface microorganism dynamic detection system according to claim 1, wherein: the immune reaction module can be used for measuring the microorganism sample pretreated by the sample pretreatment module through an immunoassay method, wherein the immunoassay method is an immunochromatography method, and the specific operation steps are as follows:
firstly, selecting proper immunochromatographic test paper;
adding a proper amount of buffer solution into the microorganism sample pretreated by the sample pretreatment module for dilution, and applying the buffer solution containing the microorganism sample to immunochromatography test paper;
introducing active chemical groups on the carrier to enable the active chemical groups to be covalently combined with amino acid residues on the antibody, so that the specific antibody solid-phase probe is fixed on the carrier;
at specific locations in the immunochromatographic material, microorganisms in the microorganism sample will bind to the solid-phase probes, eventually forming specific antigen-antibody complexes.
4. A surgical instrument surface microorganism dynamic detection system according to claim 1, wherein: the fluorescent labeling module performs immune reaction with the combined specific antibody in the immune reaction module through the secondary antibody with fluorescent label, so that the antigen on the surface of the microbial sample is fluorescently labeled, and the specific operation steps are as follows:
selecting a corresponding secondary antibody according to the type of the specific antibody solid-phase probe used in the immune reaction module;
selecting a proper fluorescent probe or a labeled antibody according to the type of the selected secondary antibody, wherein the specific fluorescent probe is fluorescent protein;
chemically conjugating the secondary antibody with the fluorescent substance to form a conjugated compound with the fluorescent substance;
mixing the labeled fluorescent secondary antibody with a microbial sample to be detected to form an immunoreaction mixture, and properly diluting according to the concentration of the marker;
the immunoreaction mixture is subjected to immunoreaction under proper conditions, so that the labeled fluorescent secondary antibody is specifically combined with the target microorganism.
5. A surgical instrument surface microorganism dynamic detection system according to claim 4, wherein: the method for chemically conjugating the secondary antibody and the fluorescent substance to form conjugated complex of the fluorescent substance and the secondary antibody comprises the following specific operation steps:
activating the fluorescent protein with an appropriate activator according to its chemical properties;
reacting the secondary antibody with an active chemical group to enable the secondary antibody to have reactivity of chemical coupling with fluorescent protein;
mixing the activated fluorescent protein and the activated secondary antibody, and performing chemical coupling reaction under proper conditions, wherein the chemical coupling reaction comprises the reaction of an amino group and isocyanate, and the reaction of a sulfhydryl group and maleimide;
the chemically coupled product was purified to remove impurities and the product was further observed and detected using a spectrophotometer.
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