CN113517025B - Pathogen online monitoring system and method - Google Patents

Pathogen online monitoring system and method Download PDF

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CN113517025B
CN113517025B CN202010280610.7A CN202010280610A CN113517025B CN 113517025 B CN113517025 B CN 113517025B CN 202010280610 A CN202010280610 A CN 202010280610A CN 113517025 B CN113517025 B CN 113517025B
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夏涵
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Abstract

The invention provides a pathogen online monitoring system and a method, wherein the monitoring system comprises: the detection module is used for detecting a plurality of pathogens in a sample to be detected and acquiring a detection result aiming at the content of each pathogen; the cloud server is used for uploading the detection result obtained by the detection module to the cloud in real time for storage, and analyzing the detection result to determine whether the detection result contains abnormal information; and the alarm module is used for alarming the abnormal information in the detection result acquired by the detection module according to the analysis processing of the cloud server. According to the pathogen online monitoring system, the detection result of the pathogen is obtained through the detection module and is uploaded to the cloud end in real time for storage and analysis, so that the pathogen online monitoring can be realized, the abnormal condition can be found in time conveniently, measures can be taken, isolation after outbreak of epidemic situation is found is avoided, and the biological defense capacity is improved.

Description

Pathogen online monitoring system and method
Technical Field
The invention relates to the technical field of on-line monitoring, in particular to a pathogen on-line monitoring system and method.
Background
With the rapid development of economy, the number and scale of cities are greatly increased, and more people in cities gather to refract the severity of the health problems of urban population in China.
Infectious disease prevention and control is also an important aspect in the urban population health safety prevention and control system, and refers to prevention, treatment and epidemic control of infectious diseases in the public health field by taking the national disease control centers at all levels as the main comprehensive medical and health departments, administrative management departments at all levels and all social circles. Infectious disease prevention and control the focus of infectious disease prevention is to investigate the presence of infectious diseases in our surrounding environment, determine the source of the disease, and understand the main host and main vector of the disease through close monitoring before the disease affects humans. At present, the industry basically recognizes and masters the transmission route and mode, transmission and epidemic links, course change of infectious diseases and the like of infectious diseases, discovers and confirms specific pathogens, and also discovers that the infectious diseases have epidemiological characteristics and show the characteristics of epidemiology (distribution, epidemic, pandemic and outbreak), regionality, seasonality, periodicity and the like. Meanwhile, the weakest link in the process is found by continuously knowing the natural circulation process of each infectious disease. In addition, in the current stage, aiming at the common lack of effective prevention and treatment means for new and severe infectious diseases, most of the new and severe infectious diseases are isolated after epidemic outbreak is found, and an effective monitoring and early warning mechanism is lacked.
The development and application of the field detection technology can detect pathogens in real time, and is important for taking measures in time and preventing and controlling epidemic spread. Because the characterization of the biological safety threat emergency can be various, a multi-angle and multi-level information platform is required to support, a center for comprehensive analysis of the biological threat emergency information is formed, information of all aspects is integrated, related departments are informed in time, and an analysis result, early warning and prompt information are provided so as to effectively respond. The information early warning system for early identifying the bio-threat event, so that the establishment and the response of the system are important contents for the construction of national bio-defense capability.
Disclosure of Invention
The invention provides a pathogen online monitoring system and a pathogen online monitoring method, which are used for realizing the online monitoring of pathogens.
The invention provides a pathogen online monitoring system, which comprises:
the detection module is used for detecting a plurality of pathogens in a sample to be detected and acquiring a detection result aiming at the content of each pathogen;
the cloud server is used for uploading the detection result obtained by the detection module to a cloud in real time for storage, and analyzing the detection result to determine whether the detection result contains abnormal information;
and the alarm module is used for alarming the abnormal information in the detection result acquired by the detection module according to the analysis processing of the cloud server.
Further, the detection module includes:
a selection unit for selecting a pathogen-specific primer pair corresponding to each of the pathogens in the number of pathogens, wherein the pathogen-specific primer pair is capable of mediating extension of a selected polynucleotide extension product of known length from a nucleic acid pathogen-specific target of the respective pathogen;
a contacting unit for contacting nucleic acids from said sample to be tested with a number of said pathogen-specific primer pairs in a reaction mixture in an expansion step under conditions promoting expansion of polynucleotide expansion products;
a separation detection unit for taking out aliquots of the reaction mixture at preset intervals during the extension step, separating the extension products in each aliquot, then detecting the extension products in each aliquot, quantifying the detected extension products, and determining the content of pathogens present in the sample to be detected according to the detected content of the extension products.
Further, the extension products separated by the separation detection unit are separated by a metagenome high-throughput sequencing method, and the extension products are detected by a nucleic acid binding dye comprising a detectable label.
Further, the detectable label is selected from the group consisting of a fluorescent label, a radioactive label, a colorimetric label, a magnetic label, and an enzymatic label.
Further, the sample to be tested comprises body fluid or tissue of an animal, and the pathogen comprises a virus, a bacterium or a protozoan.
Further, the cloud server comprises an analysis module and a database module,
the database module is used for storing the detection result aiming at the content of each pathogen acquired by the detection module and a preset threshold value of the content of each pathogen in the plurality of pathogens;
the analysis module is used for comparing the detection result aiming at the content of each pathogen with a preset threshold value of each pathogen and determining whether the detection result contains abnormal information according to the comparison result, wherein,
when the detection result exceeds the preset threshold value, judging that the detection result contains abnormal information,
and when the detection result does not exceed the preset threshold, judging that the detection result does not contain abnormal information.
Further, the pathogen online monitoring system further comprises a pretreatment module for pretreating the sample to be detected, and the pretreatment comprises one or more of magnetic selection, centrifugation, sedimentation and filtration.
Further, the pathogen online monitoring system further comprises: at least one processor and at least one non-transitory processor-readable medium storing at least one of processor-executable instructions or data, and the processor performing the steps of:
detecting a plurality of pathogens in a sample to be detected, and acquiring a detection result aiming at each pathogen;
uploading the obtained detection result to a cloud end in real time for storage, and analyzing and processing the detection result to determine whether the detection result contains abnormal information;
and according to the analysis processing of the cloud server, alarming the acquired abnormal information in the monitoring result.
Further, the cloud server, when analyzing the inspection result to determine whether there is any abnormal information in the inspection result, executes the following steps:
firstly, obtaining P pieces of detection sample information which is determined whether to contain abnormal information, wherein each piece of sample information contains a detection result of N pathogen contents detected by a detection module and the determined sample contains the abnormal information, forming a historical detection result matrix X by the detection results of the N pathogen contents of the P samples, wherein the row of the historical detection result matrix X is provided with P rows and N columns, meanwhile, the corresponding P samples contain the abnormal information to form an abnormal result vector Y, the row of the vector Y is provided with P values, each value represents whether the abnormal information exists in one sample, when the detection sample contains the abnormal information, the abnormal detection result corresponding to the sample is 1, and when the detection sample does not contain the abnormal information, the abnormal detection result corresponding to the sample is 0;
secondly, constructing an abnormal learning function for the detection result matrix X and the abnormal result vector Y;
Figure BDA0002446437290000041
wherein f (X) is the constructed abnormal learning function, YiFor the ith value, X, of the exception result vector Yi,jFor the value of the ith row and jth column of the detection result matrix X, λ0For anomalous constant terms to be solved, λjFor pathogen coefficients to be solved, i is 1, 2, 3 … P, j is 1, 2, 3 … N;
then, an abnormal constant term λ is determined0And pathogen coefficient lambdajThe specific determination method is as follows:
order:
Figure BDA0002446437290000042
wherein,
Figure BDA0002446437290000051
learning function f (X) for anomaliesConstant term λ0The partial derivatives are made to the surface of the steel,
Figure BDA0002446437290000052
for abnormal learning function f (X) versus pathogen coefficient lambdajMaking partial derivatives, and forming N +1 equation sets by using the above expression, wherein the equation sets contain abnormal constant term lambda0And N pathogen coefficients λjThe total number of N +1 unknowns is calculated, and then the abnormal constant term lambda can be obtained by solving the equation set0And N pathogen coefficients λj；
Finally, the cloud server determines whether abnormal information is contained according to the detection result obtained by the detection module;
Figure BDA0002446437290000053
wherein P is the result-determined value, KjIs the detection result of the content of the jth pathogen obtained by the detection module, and
Figure BDA0002446437290000054
if the detection result obtained by the detection module does not contain abnormal information, if the detection result does not contain abnormal information, the detection module obtains abnormal information
Figure BDA0002446437290000055
And if the detection result obtained by the detection module contains abnormal information, the alarm module gives an alarm for the abnormal information.
The pathogen online monitoring system provided by the embodiment of the invention has the following beneficial effects: the detection result of the pathogen is acquired through the detection module and is uploaded to the cloud for storage and analysis in real time, so that the online monitoring of the pathogen can be realized, the abnormal condition can be found in time and measures can be taken conveniently, the isolation after the outbreak of the epidemic situation is found is avoided, and the biological defense capability is improved.
The invention also provides an on-line pathogen monitoring method, which comprises the following steps:
step 1: detecting a plurality of pathogens in a sample to be detected, and acquiring a detection result aiming at each pathogen;
step 2: uploading the obtained detection result to a cloud end in real time for storage, and analyzing and processing the detection result to determine whether the detection result contains abnormal information;
and step 3: and according to the analysis processing of the cloud server, alarming the acquired abnormal information in the monitoring result.
The pathogen online monitoring method provided by the embodiment of the invention has the following beneficial effects: the detection result of the pathogen is obtained and uploaded to the cloud for storage and analysis in real time, so that the online monitoring of the pathogen can be realized, the abnormal condition can be found in time conveniently, measures can be taken, isolation after outbreak of epidemic situation is found is avoided, and the biological defense capability is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of an on-line pathogen monitoring system according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a pathogen online monitoring method according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
An embodiment of the present invention provides an online pathogen monitoring system, as shown in fig. 1, including:
the detection module 101 is configured to detect a plurality of pathogens in a sample to be detected, and obtain a detection result for the content of each pathogen;
the cloud server 102 is configured to upload the detection result obtained by the detection module 101 to a cloud in real time for storage, and analyze the detection result to determine whether the detection result contains abnormal information;
the alarm module 103 is configured to alarm the abnormal information in the detection result obtained by the detection module 101 according to the analysis processing of the cloud server 102.
The working principle of the technical scheme is as follows: the detection module 101 detects a plurality of pathogens in a sample to be detected, and obtains a detection result aiming at the content of each pathogen; the cloud server 102 uploads the detection result obtained by the detection module 101 to the cloud in real time for storage, and analyzes the detection result to determine whether the detection result contains abnormal information; the alarm module 103 alarms the abnormal information in the detection result obtained by the detection module according to the analysis processing of the cloud server.
The alarm module 103 can notify technicians of epidemic prevention and control departments of timely handling abnormal situations through a buzzer, short message reminding and WeChat reminding.
The beneficial effects of the above technical scheme are: the detection result of the pathogen is acquired through the detection module and is uploaded to the cloud for storage and analysis in real time, so that the online monitoring of the pathogen can be realized, the abnormal condition can be found in time and measures can be taken conveniently, the isolation after the outbreak of the epidemic situation is found is avoided, and the biological defense capability is improved.
In one embodiment, the detection module comprises:
a selection unit for selecting a pathogen-specific primer pair corresponding to each of the pathogens in the number of pathogens, wherein the pathogen-specific primer pair is capable of mediating extension of a selected polynucleotide extension product of known length from a nucleic acid pathogen-specific target of the respective pathogen;
a contacting unit for contacting nucleic acids from said sample to be tested with a number of said pathogen-specific primer pairs in a reaction mixture in an expansion step under conditions promoting expansion of polynucleotide expansion products;
a separation detection unit for taking out aliquots of the reaction mixture at preset intervals during the extension step, separating the extension products in each aliquot, then detecting the extension products in each aliquot, quantifying the detected extension products, and determining the content of pathogens present in the sample to be detected according to the detected content of the extension products.
The working principle of the technical scheme is as follows: a selection unit selects a pathogen-specific primer pair corresponding to each pathogen of a number of pathogens, wherein the pathogen-specific primer pair is capable of mediating extension of a selected polynucleotide extension product of known length from a nucleic acid pathogen-specific target of the respective pathogen; a contacting unit for contacting nucleic acids from a sample to be tested with a number of pathogen-specific primer pairs in a reaction mixture in an expansion step under conditions promoting expansion of a polynucleotide expansion product; the separation detection unit takes out aliquots of the reaction mixture at preset intervals during the extension step, separates the extension products in each aliquot, then detects the extension products in each aliquot, quantifies the detected extension products, and determines the content of pathogens present in the sample to be detected according to the detected content of the extension products.
Wherein each pair of primers corresponds to a nucleic acid sequence specific to the respective pathogen, and the presence and amount of the extension products is determined by splitting a portion of the extension mixture to detect the extension products. Where an extension product refers to a polynucleotide that is a copy of a particular polynucleotide produced in an extension reaction, the extension product may be DNA or RNA, and may be double-stranded or single-stranded.
Further, amplification formats include, but are not limited to: polymerase Chain Reaction (PCR), Ligase Chain Reaction (LCR), transcriptional extension, self-sustained sequence extension, nucleic acid-based sequence extension.
The beneficial effects of the above technical scheme are: by means of the selection unit, the contact unit and the separation detection unit, the detection module can determine the content of pathogens existing in the sample to be detected according to the content of the detected extension products, and further monitor the occurrence and development of diseases.
In one embodiment, the extension products isolated by the isolation detection unit are isolated using metagenomic high-throughput sequencing methods and the extension products are detected by a nucleic acid binding dye comprising a detectable label.
The working principle of the technical scheme is as follows: and (2) separating the extended products separated by the separation detection unit by adopting a DNA sequencer adopting a metagenome high-throughput sequencing technology, illustratively, the DNA sequencer is an Illumina company second-generation sequencing platform Miniseq instrument, mixing all the extended products separated by the separation detection unit, wherein each extended product is about 1ng, and 20 extended products are obtained in total, then, sequencing is carried out by the Illumina company second-generation sequencing platform Miniseq instrument, the required data volume is more than 5Gb, and the running time of the step is about 24 hours. The Miniseq sequencer has the advantages of high accuracy, high flux, high sensitivity, low running cost and the like, and can complete traditional genomics research (sequencing and annotation) and functional genomics (gene expression and regulation, gene function and protein/nucleic acid interaction) research simultaneously. High throughput sequencing can read 40 to 400 million sequences in one experiment, as opposed to 96-channel capillary sequencing, which is conventional sequencing. The reading length is from 25bp to 450bp according to different platforms, different sequencing platforms can read the number of bases from 1G to 14G in one experiment, and thus the huge sequencing capacity is incomparable with the traditional sequencer.
The beneficial effects of the above technical scheme are: the separation of the extension products can be realized by a metagenome high-throughput sequencing method, and the detection of the extension products is realized by a nucleic acid binding dye containing a detectable label.
In one embodiment, the detectable label is selected from the group consisting of a fluorescent label, a radioactive label, a colorimetric label, a magnetic label, or an enzymatic label.
The working principle of the technical scheme is as follows: different types of signal characteristics may be utilized, including: fluorescence, scattered light, light polarization, radio waves, particle size, magnetic field, chemiluminescence, and radioactivity. As an example, the detectable label is a fluorescent label, which may be a label or dye that intercalates or binds to the expanded nucleic acid molecule to thereby emit a signal.
The beneficial effects of the above technical scheme are: labeling with fluorescence, radioactivity, colorimetry, magnetism, or enzymes can reduce costs compared to labeling nucleotides.
In one embodiment, the sample to be tested comprises a body fluid or tissue of an animal and the pathogen comprises a virus, a bacterium or a protozoan.
The working principle of the technical scheme is as follows: the sample to be tested is of mammalian origin, e.g. human. Alternatively, the sample to be tested may be derived from a vertebrate.
The sample to be detected comprises: urine, blood, skin, plasma, serum, saliva, wound tissue, wound exudate, biopsy, stool, solid tissue, and the like. The sample to be detected is derived from: respiratory tract, urogenital tract, genital tract, central nervous system, etc.
It is noted that the sample to be tested may be derived from a plant. And the sample to be tested can also be obtained from the air or water in the environment, or from a surface in contact with the environment.
By way of example, the pathogens include cytomegalovirus, human polyoma virus, human herpesvirus, aspergillus flavus, aspergillus glaucus, aspergillus niger, mucor racemosus a, mucor racemosus B, oospora lactis, penicillium expansum, penicillium roqueforti, penicillium digitatum, rhizopus nigricans, and the like.
The beneficial effects of the above technical scheme are: the pathogen online monitoring system realizes online monitoring of a plurality of pathogens from different sources.
In one embodiment, the cloud server comprises an analysis module and a database module,
the database module is used for storing the detection result aiming at the content of each pathogen acquired by the detection module and a preset threshold value of the content of each pathogen in the plurality of pathogens;
the analysis module is used for comparing the detection result aiming at the content of each pathogen with a preset threshold value of each pathogen and determining whether the detection result contains abnormal information according to the comparison result, wherein,
when the detection result exceeds the preset threshold value, judging that the detection result contains abnormal information,
and when the detection result does not exceed the preset threshold, judging that the detection result does not contain abnormal information.
The working principle of the technical scheme is as follows: the database module stores the detection result aiming at the content of each pathogen acquired by the detection module and a preset threshold value of the content of each pathogen in a plurality of pathogens; and the analysis module compares the detection result aiming at the content of each pathogen with a preset threshold value of each pathogen and determines whether the detection result contains abnormal information or not according to the comparison result.
The beneficial effects of the above technical scheme are: by means of the database module and the analysis module, the storage and comparison of the detection results can be realized, the abnormal information in the detection results can be found conveniently in time, and the alarm is given.
In one embodiment, the pathogen online monitoring system further comprises a pretreatment module 104 for pretreating the sample to be detected, and the pretreatment comprises a combination of one or more of magnetic selection, centrifugation, sedimentation, and filtration.
The working principle of the technical scheme is as follows: the magnetic selection utilizes the gravity settling principle of metal particles; centrifugation can be carried out in an airborne core machine; sedimentation is natural sedimentation by gravity; filtration the sample to be treated can be filtered on a black polycarbonate film in a vacuum environment.
The beneficial effects of the above technical scheme are: after the sample to be detected is pretreated, the sample to be detected is convenient to detect, and the detection efficiency and accuracy are improved.
In one embodiment, the pathogen online monitoring system further comprises: at least one processor and at least one non-transitory processor-readable medium storing at least one of processor-executable instructions or data, and the processor performing the steps of:
detecting a plurality of pathogens in a sample to be detected, and acquiring a detection result aiming at each pathogen;
uploading the obtained detection result to a cloud end in real time for storage, and analyzing and processing the detection result to determine whether the detection result contains abnormal information;
and according to the analysis processing of the cloud server, alarming the acquired abnormal information in the monitoring result.
The working principle of the technical scheme is as follows: the non-transitory processor-readable medium may store information in one or more data structures. The data structures may take various forms, such as records associated with a relational database, the database itself, a lookup table, and so forth. Data structures may store a variety of different information or data. Examples of non-transitory processor-readable media include, but are not limited to, the following: recordable type media such as portable disks and memory, hard drives, CD/DVD ROMs, digital tapes, computer memory; and other non-transitory computer readable storage media.
The processor may be any logic processing unit, such as one or more Central Processing Units (CPUs), Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), Programmable Logic Controllers (PLCs), artificial neural network circuits or systems, or any other logic component.
The beneficial effects of the above technical scheme are: the pathogen online monitoring system of the present invention is facilitated to enable online monitoring of pathogens via a processor and a non-transitory processor readable medium.
In one embodiment, the cloud server, when analyzing the inspection result to determine whether there is any abnormal information in the inspection result, performs the following steps:
firstly, obtaining P pieces of detection sample information which is determined whether to contain abnormal information, wherein each piece of sample information contains a detection result of N pathogen contents detected by a detection module and the determined sample contains the abnormal information, forming a historical detection result matrix X by the detection results of the N pathogen contents of the P samples, wherein the row of the historical detection result matrix X is provided with P rows and N columns, meanwhile, the corresponding P samples contain the abnormal information to form an abnormal result vector Y, the row of the vector Y is provided with P values, each value represents whether the abnormal information exists in one sample, when the detection sample contains the abnormal information, the abnormal detection result corresponding to the sample is 1, and when the detection sample does not contain the abnormal information, the abnormal detection result corresponding to the sample is 0;
secondly, constructing an abnormal learning function for the detection result matrix X and the abnormal result vector Y;
Figure BDA0002446437290000121
wherein f (X) is the constructed abnormal learning function, YiFor the ith value, X, of the exception result vector Yi,jFor the value of the ith row and jth column of the detection result matrix X, λ0For anomalous constant terms to be solved, λjFor pathogen coefficients to be solved, i is 1, 2, 3 … P, j is 1, 2, 3 … N;
Yithe ith value of the abnormal result vector Y is the value of whether the ith sample in the P detection samples which are determined to contain abnormal information contains abnormal information;
Xi,jis the value of the ith row and the jth column of the detection result matrix X, i.e. P ones have been obtainedDetermining whether the detection result of the content of the jth pathogen of the ith sample in the detection samples containing the abnormal information;
then, an abnormal constant term λ is determined0And pathogen coefficient lambdajThe specific determination method is as follows:
order:
Figure BDA0002446437290000131
wherein,
Figure BDA0002446437290000132
for the anomalous learning function f (X) to the anomalous constant term λ0The partial derivatives are made to the surface of the steel,
Figure BDA0002446437290000133
for abnormal learning function f (X) versus pathogen coefficient lambdajMaking partial derivatives, and forming N +1 equation sets by using the above expression, wherein the equation sets contain abnormal constant term lambda0And N pathogen coefficients λjThe total number of N +1 unknowns is calculated, and then the abnormal constant term lambda can be obtained by solving the equation set0And N pathogen coefficients λj；
Finally, the cloud server determines whether abnormal information is contained according to the detection result obtained by the detection module;
Figure BDA0002446437290000134
wherein P is the result-determined value, KjIs the detection result of the content of the jth pathogen obtained by the detection module, and
Figure BDA0002446437290000135
if the detection result obtained by the detection module does not contain abnormal information, if the detection result does not contain abnormal information, the detection module obtains abnormal information
Figure BDA0002446437290000136
The detection result obtained by the time detection module contains abnormityAnd if the information is received, the alarm module gives an alarm for abnormal information.
The beneficial effects of the above technical scheme are: by utilizing the technology, whether abnormal information exists in a sample to be detected can be determined according to the detection results of a plurality of pathogens acquired by a detection module, an alarm is given when the abnormal information exists, and meanwhile, when the abnormal information exists, the individual judgment is not carried out according to each pathogen, so that an early warning is given only when the content of a certain pathogen reaches a preset threshold value, but all the pathogens in the sample to be detected are judged as a whole, thereby enhancing the comprehensiveness of the judgment, avoiding the situation that the content of a plurality of pathogens is very high but does not reach the threshold value, leading the monitored abnormal result to be more scientific, simultaneously, when the abnormal information is determined to exist in the detection results, not only the human intervention is not needed, but also a detection model can be obtained through the autonomous learning of the information of the detection sample which is determined to contain the abnormal information before the monitoring, after the detection result of the sample to be detected is obtained, whether the abnormal information exists can be determined only by substituting the detection result into the model, so that the detection efficiency is greatly improved.
The embodiment of the invention also provides an on-line pathogen monitoring method, which comprises the following steps:
step 1: detecting a plurality of pathogens in a sample to be detected, and acquiring a detection result aiming at each pathogen;
step 2: uploading the obtained detection result to a cloud end in real time for storage, and analyzing and processing the detection result to determine whether the detection result contains abnormal information;
and step 3: and according to the analysis processing of the cloud server, alarming the acquired abnormal information in the monitoring result.
The working principle of the technical scheme is as follows: detecting pathogens in a sample to be detected, and acquiring a detection result aiming at each pathogen; uploading the detection result to a cloud end in real time for storage, analyzing and processing the detection result, and determining whether the detection result contains abnormal information; and according to the analysis and processing of the cloud server, alarming abnormal information in the monitoring result.
The beneficial effects of the above technical scheme are: the detection result of the pathogen is obtained and uploaded to the cloud for storage and analysis in real time, so that the online monitoring of the pathogen can be realized, the abnormal condition can be found in time conveniently, measures can be taken, isolation after outbreak of epidemic situation is found is avoided, and the biological defense capability is improved.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (9)

1. An online pathogen monitoring system, comprising:
the detection module is used for detecting a plurality of pathogens in a sample to be detected and acquiring a detection result aiming at the content of each pathogen;
the cloud server is used for uploading the detection result obtained by the detection module to a cloud in real time for storage, and analyzing the detection result to determine whether the detection result contains abnormal information;
the alarm module is used for alarming the abnormal information in the detection result acquired by the detection module according to the analysis processing of the cloud server;
the cloud server performs the following steps when analyzing the inspection result to determine whether the inspection result contains abnormal information:
first, obtain
Figure 217251DEST_PATH_IMAGE001
Sample information for detection, each of which contains information detected by the detection module, the sample information having been determined to contain abnormal information
Figure 291649DEST_PATH_IMAGE002
The detection result of the content of the seed pathogen and the determined whether the sample contains abnormal information are detected
Figure 602544DEST_PATH_IMAGE001
Of a sample
Figure 404278DEST_PATH_IMAGE002
Forming a historical detection result matrix by the detection result of the content of the seed pathogen
Figure 484230DEST_PATH_IMAGE003
The historical detection result matrix
Figure 962484DEST_PATH_IMAGE003
Go with
Figure 760676DEST_PATH_IMAGE001
Line of
Figure 366101DEST_PATH_IMAGE002
In a row, at the same time will
Figure 566138DEST_PATH_IMAGE001
Whether the sample contains abnormal information or not forms an abnormal result vector
Figure 825081DEST_PATH_IMAGE004
Said vector
Figure 736667DEST_PATH_IMAGE004
In the middle, there are
Figure 739259DEST_PATH_IMAGE001
Values, each value representing whether abnormal information exists in a sample, and when the detected sample contains abnormal information, the abnormal detection corresponding to the sampleThe test result is 1, and when the detected sample does not contain abnormal information, the abnormal test result corresponding to the sample is 0;
secondly, for the detection result matrix
Figure 669168DEST_PATH_IMAGE003
And abnormal result vector
Figure 630171DEST_PATH_IMAGE004
Constructing an abnormal learning function;
Figure 527589DEST_PATH_IMAGE005
wherein,
Figure 68292DEST_PATH_IMAGE006
in order to construct the abnormal learning function,
Figure 242921DEST_PATH_IMAGE007
as vectors of abnormal results
Figure 719033DEST_PATH_IMAGE004
To (1) a
Figure 979113DEST_PATH_IMAGE008
The value of the one or more of the one,
Figure 323507DEST_PATH_IMAGE009
is a matrix of detection results
Figure 990460DEST_PATH_IMAGE010
To (1) a
Figure 293265DEST_PATH_IMAGE008
Go to the first
Figure 384849DEST_PATH_IMAGE011
The value of the column is such that,
Figure 64092DEST_PATH_IMAGE012
in order to solve for the anomalous constant term that needs to be solved,
Figure 72368DEST_PATH_IMAGE013
in order to solve the pathogen coefficients to be solved,
Figure DEST_PATH_IMAGE015A
，
Figure DEST_PATH_IMAGE017A
；
then, an abnormal constant term is determined
Figure 375436DEST_PATH_IMAGE012
And pathogen coefficient
Figure 344529DEST_PATH_IMAGE013
The specific determination method is as follows:
order:
Figure DEST_PATH_IMAGE019A
wherein,
Figure 358621DEST_PATH_IMAGE020
learning functions for anomalies
Figure 96770DEST_PATH_IMAGE021
For abnormal constant term
Figure 741378DEST_PATH_IMAGE012
The partial derivatives are made to the surface of the steel,
Figure 807554DEST_PATH_IMAGE022
learning functions for anomalies
Figure 563021DEST_PATH_IMAGE021
Coefficient of pathogen
Figure 890097DEST_PATH_IMAGE013
By making a partial derivative, the above expression can be used to form
Figure 331705DEST_PATH_IMAGE023
A system of equations containing abnormal constant terms
Figure 275390DEST_PATH_IMAGE012
And
Figure 444334DEST_PATH_IMAGE024
individual pathogen coefficient
Figure 157075DEST_PATH_IMAGE013
In total
Figure 2540DEST_PATH_IMAGE023
The unknown quantity can be obtained by solving the equation set
Figure 433522DEST_PATH_IMAGE012
And
Figure 406157DEST_PATH_IMAGE024
individual pathogen coefficient
Figure 973405DEST_PATH_IMAGE013
；
Finally, the cloud server determines whether abnormal information is contained according to the detection result obtained by the detection module;
Figure 865137DEST_PATH_IMAGE025
wherein,
Figure 409513DEST_PATH_IMAGE001
in order to determine the value for the result,
Figure 310473DEST_PATH_IMAGE026
acquired for the detection module
Figure 342014DEST_PATH_IMAGE028
The detection result of the content of the individual pathogens, and
Figure 670227DEST_PATH_IMAGE029
if the detection result obtained by the detection module does not contain abnormal information, if the detection result does not contain abnormal information, the detection module obtains abnormal information
Figure 934856DEST_PATH_IMAGE030
And if the detection result obtained by the time detection module contains abnormal information, the alarm module alarms the abnormal information.
2. The pathogen online monitoring system of claim 1, wherein the detection module comprises:
a selection unit for selecting a pathogen-specific primer pair corresponding to each of the pathogens in the number of pathogens, wherein the pathogen-specific primer pair is capable of mediating extension of a selected polynucleotide extension product of known length from a nucleic acid pathogen-specific target of the respective pathogen;
a contacting unit for contacting nucleic acids from said sample to be tested with a number of said pathogen-specific primer pairs in a reaction mixture in an expansion step under conditions promoting expansion of polynucleotide expansion products;
a separation detection unit for taking out aliquots of the reaction mixture at preset intervals during the extension step, separating the extension products in each aliquot, then detecting the extension products in each aliquot, quantifying the detected extension products, and determining the content of pathogens present in the sample to be detected according to the detected content of the extension products.
3. The pathogen online monitoring system of claim 2, wherein the extension products isolated by the isolation detection unit are isolated using metagenomic high-throughput sequencing methods and the extension products are detected by a nucleic acid binding dye comprising a detectable label.
4. The on-line pathogen monitoring system of claim 3 wherein the detectable label is selected from the group consisting of a fluorescent label, a radioactive label, a colorimetric label, a magnetic label and an enzymatic label.
5. The on-line pathogen monitoring system according to claim 1, wherein the sample to be tested comprises body fluids or tissues of animals and the pathogen comprises a virus, a bacterium or a protozoan.
6. The pathogen online monitoring system of claim 1, wherein the cloud server comprises an analysis module and a database module,
the database module is used for storing the detection result aiming at the content of each pathogen acquired by the detection module and a preset threshold value of the content of each pathogen in the plurality of pathogens;
the analysis module is used for comparing the detection result aiming at the content of each pathogen with a preset threshold value of each pathogen and determining whether the detection result contains abnormal information according to the comparison result, wherein,
when the detection result exceeds the preset threshold value, judging that the detection result contains abnormal information,
and when the detection result does not exceed the preset threshold, judging that the detection result does not contain abnormal information.
7. The pathogen online monitoring system of claim 1, further comprising a pretreatment module for pretreating the sample to be tested, wherein the pretreatment comprises a combination of one or more of magnetic selection, centrifugation, sedimentation, and filtration.
8. The pathogen online monitoring system of claim 1, further comprising: at least one processor and at least one non-transitory processor-readable medium storing at least one of processor-executable instructions or data, and the processor performing the steps of:
detecting a plurality of pathogens in a sample to be detected, and acquiring a detection result aiming at each pathogen;
uploading the obtained detection result to a cloud end in real time for storage, and analyzing and processing the detection result to determine whether the detection result contains abnormal information;
and alarming the abnormal information in the acquired detection result according to the analysis processing of the cloud server.
9. An on-line pathogen monitoring method, characterized in that the monitoring method performs the following steps:
step 1: detecting a plurality of pathogens in a sample to be detected, and acquiring a detection result aiming at each pathogen;
step 2: uploading the obtained detection result to a cloud end in real time for storage, and analyzing and processing the detection result to determine whether the detection result contains abnormal information;
and step 3: according to the analysis processing of the cloud server, alarming is carried out on the abnormal information in the obtained detection result;
when the test result is analyzed to determine whether abnormal information exists in the test result, the following steps are executed:
first, obtain
Figure 373927DEST_PATH_IMAGE001
Sample information for detection, each of which contains information detected by the detection module, the sample information having been determined to contain abnormal information
Figure 915767DEST_PATH_IMAGE002
The detection result of the content of the seed pathogen and the determined whether the sample contains abnormal information are detected
Figure 290248DEST_PATH_IMAGE001
Of a sample
Figure 917538DEST_PATH_IMAGE002
Forming a historical detection result matrix by the detection result of the content of the seed pathogen
Figure 786399DEST_PATH_IMAGE003
The historical detection result matrix
Figure 917166DEST_PATH_IMAGE003
Go with
Figure 462548DEST_PATH_IMAGE001
Line of
Figure 577135DEST_PATH_IMAGE002
In a row, at the same time will
Figure 92430DEST_PATH_IMAGE001
Whether the sample contains abnormal information or not forms an abnormal result vector
Figure 733495DEST_PATH_IMAGE004
Said vector
Figure 308833DEST_PATH_IMAGE004
In the middle, there are
Figure 786082DEST_PATH_IMAGE001
Values, each value represents whether abnormal information exists in a sample, when a detection sample contains abnormal information, the abnormal detection result corresponding to the sample is 1, and when the detection sample does not contain abnormal information, the abnormal detection result corresponding to the sample is 0;
secondly, for the detection result matrix
Figure 370647DEST_PATH_IMAGE003
And abnormal result vector
Figure 476006DEST_PATH_IMAGE004
Constructing an abnormal learning function;
Figure 113923DEST_PATH_IMAGE031
wherein,
Figure 671944DEST_PATH_IMAGE006
in order to construct the abnormal learning function,
Figure 794621DEST_PATH_IMAGE007
as vectors of abnormal results
Figure 895432DEST_PATH_IMAGE004
To (1) a
Figure 78151DEST_PATH_IMAGE008
The value of the one or more of the one,
Figure 513681DEST_PATH_IMAGE009
is a matrix of detection results
Figure 440048DEST_PATH_IMAGE010
To (1) a
Figure 254421DEST_PATH_IMAGE008
Go to the first
Figure 748987DEST_PATH_IMAGE011
The value of the column is such that,
Figure 281600DEST_PATH_IMAGE012
in order to solve for the anomalous constant term that needs to be solved,
Figure 637757DEST_PATH_IMAGE013
in order to solve the pathogen coefficients to be solved,
Figure DEST_PATH_IMAGE015AA
，
Figure DEST_PATH_IMAGE017AA
；
then, an abnormal constant term is determined
Figure 165690DEST_PATH_IMAGE012
And pathogen coefficient
Figure 690212DEST_PATH_IMAGE013
The specific determination method is as follows:
order:
Figure DEST_PATH_IMAGE019AA
wherein,
Figure 913383DEST_PATH_IMAGE020
learning functions for anomalies
Figure 542073DEST_PATH_IMAGE021
For abnormal constant term
Figure 596617DEST_PATH_IMAGE012
The partial derivatives are made to the surface of the steel,
Figure DEST_PATH_IMAGE033AAAA
learning functions for anomalies
Figure 229723DEST_PATH_IMAGE021
Coefficient of pathogen
Figure 595983DEST_PATH_IMAGE013
By making a partial derivative, the above expression can be used to form
Figure 667844DEST_PATH_IMAGE023
A system of equations containing abnormal constant terms
Figure 576894DEST_PATH_IMAGE012
And
Figure 318585DEST_PATH_IMAGE002
individual pathogen coefficient
Figure 313086DEST_PATH_IMAGE013
In total
Figure 537439DEST_PATH_IMAGE023
The unknown quantity can be obtained by solving the equation set
Figure 300995DEST_PATH_IMAGE012
And
Figure 338221DEST_PATH_IMAGE002
individual pathogen coefficient
Figure 695385DEST_PATH_IMAGE013
；
Finally, the cloud server determines whether abnormal information is contained according to the detection result obtained by the detection module;
Figure 109048DEST_PATH_IMAGE025
wherein,
Figure 727112DEST_PATH_IMAGE001
in order to determine the value for the result,
Figure 325452DEST_PATH_IMAGE026
acquired for the detection module
Figure 28966DEST_PATH_IMAGE028
The detection result of the content of the individual pathogens, and
Figure 856107DEST_PATH_IMAGE029
if the detection result obtained by the detection module does not contain abnormal information, if the detection result does not contain abnormal information, the detection module obtains abnormal information
Figure 859836DEST_PATH_IMAGE030
And if the detection result obtained by the time detection module contains abnormal information, alarming the abnormal information.
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