229 results about "Mutation detection" patented technology

The invention discloses an intelligent substation communication link fault self-healing regulation and control method and system, and relates to the technical field of fault self-healing regulation and control, and the method comprises the following steps: constructing a link disturbance intensity sequence reflecting link quality fluctuation; performing trend fitting and mutation identification on the link disturbance intensity sequence based on a sliding window mechanism, and generating a link state transaction index; when the link state transaction index exceeds a first threshold value, identifying an affected key easy-to-disturb service set according to the service path topological data of the abnormal link; constructing a context-aware path migration cost model based on the key easy-to-disturb service set, and generating an optimal switching path sequence; and implementing hierarchical link regulation and control according to the path switching optimal sequence. According to the method, trend fitting and mutation detection are carried out by adopting an overlapped sliding window mechanism and a mutation scoring function, so that intermittent or hidden link degradation abnormity which is difficult to capture can be effectively identified, and a link state transaction index has higher judgment precision.

The invention relates to the technical field of power system monitoring, in particular to a bus duct temperature and humidity abnormity monitoring method, which comprises the following steps of S1, periodically acquiring temperature and humidity data through a temperature and humidity sensor, and performing data preprocessing to obtain processed data; s2, mutation detection and trend analysis are carried out according to the processed data, and mutation data and trend data are obtained respectively; s3, according to the abrupt change data and the trend data, a weighting strategy is adjusted in a self-adaptive mode; when the method is used, the credibility of data can be improved, the noise and errors of the sensor can be effectively reduced, the adaptability of the system can be improved, the data subjected to denoising and abnormal value elimination can be used as high-quality input of mutation detection, trend analysis and time sequence prediction, the accuracy of the whole temperature and humidity abnormity monitoring is improved, and the reliability of the system is improved. And short-time window detection and long-time window detection are integrated, so that the accuracy of anomaly monitoring is improved, false alarm caused by purely based on mutation detection is avoided, and the response speed of the method is improved.

The invention relates to the technical field of debugging systems, in particular to an adaptive debugging system and method for touch screen FW parameters, and the system comprises a signal acquisition module, a trend triggering module, a temperature compensation calculation module, a window generation module and a parameter output module. According to the invention, the edge of the driving voltage is carefully collected, the sliding difference algorithm is introduced to filter out high-frequency noise, meanwhile, real-time sampling of environment temperature and baseline capacitance adjustment are combined, the influence of external variables on signals can be dynamically corrected, and accurate judgment of capacitance fluctuation is improved by utilizing periodic delay deviation and regression trend analysis. By means of main frequency feature extraction and zero crossing point sudden change detection, threshold setting can be refined, self-adaptive window expansion can be achieved, touch signal response is kept sensitive and consistent all the time, adjustment and optimization can still be automatically completed even under the condition that the temperature changes frequently or signal interference is complex, parameter drift and debugging failure are effectively prevented, and user experience is improved. And the parameter automatic matching efficiency and the finished product consistency in batch production are improved.

The invention relates to the technical field of plant phenotype data processing, in particular to a plant phenotype automatic acquisition and feature processing method oriented to a multi-element environment. According to the method, the plant multi-modal phenotypic data are synchronously collected through the Internet of Things nodes, the coupling strength is calculated in combination with the environmental factors, the phenotypic parameters are divided into an environment sensitive type and a steady type, and the data classification accuracy is improved. A growth trend field is constructed by means of phenological phase decoupling analysis and geographic space superposition, abnormal segments are dynamically eliminated by combining physiological synergy degree and a mutation detection algorithm, and trend analysis stability is enhanced. A phenotype-environment response map is generated through standardized processing and an environment equivalent value model, a key environment sensitive window is recognized, a regional and staged cross-environment regulation scheme is output, intelligent and accurate management of the crop growth environment is achieved, and the method is suitable for agricultural yield increase and breeding optimization.

The invention provides a cattle behavior recognition method based on a three-axis attitude angle, and belongs to the technical field of intelligent animal husbandry management. According to the method, three-axis attitude angle signals such as the yaw angle (Yaw), the pitch angle (Pitch) and the roll angle (Roll) of the head of a cattle are collected in real time through a gyroscope sensor arranged in wearable equipment such as a cattle ear tag. The method does not depend on images, accelerometers, microphones or other modalities, only based on attitude angle signals, multiple algorithms such as a time sequence sliding window, frequency domain analysis, weighted scoring, sudden change detection and state transition modeling are utilized, and multiple typical behaviors such as ingestion, rumination, water drinking, bed lying, limping, licking, fright jumping and low activity are recognized. The method is suitable for multiple cattle breeds such as dairy cattle, beef cattle and buffalo, has the advantages of low power consumption, lightweight calculation, low deployment cost, accurate identification and the like, and is convenient for realizing behavior monitoring and health management of large-scale cattle individuals. The algorithm path can be flexibly selected, but a core input signal is only limited to attitude angle data, so that the technical boundary of the patent protection of the method is formed.

The invention discloses a student mental health prediction method based on artificial intelligence, and particularly relates to the technical field of mental health prediction. A time sequence sample fusing multi-source behavior data is constructed, a deep learning model of an abrupt change detection mechanism is combined, the ability of recognizing short-term psychological abnormal changes of students is effectively improved, psychological risk signals are dynamically output when abrupt change occurs, and an individual intervention decision module is called to generate intervention suggestions, so that the accuracy of the short-term psychological abnormal changes is improved. Closed-loop management from risk identification to intelligent intervention is realized, the early warning capability and intervention timeliness of the system for sudden psychological crisis are remarkably enhanced, and the system has relatively high practical value and popularization prospect.

The invention belongs to the technical field of power system operation control, and relates to a real-time estimation method and system for comprehensive inertia containing a network construction VSG, a storage medium and electronic equipment, and the method comprises the following steps: S1, constructing a system model; s2, inertia constant state conversion is carried out; s3, mutation detection and correction are carried out; aiming at the problem of real-time evaluation when inertia sudden change occurs in a power system, on the basis of an improved unscented Kalman filtering algorithm, conversion from parameter estimation to real-time state estimation and verification and correction of inertia sudden change conditions are realized, the real-time performance and accuracy of an inertia evaluation result are ensured, and the real-time performance of the power system is improved. And the evaluation method of inertia real-time estimation is enriched.

The invention discloses an underground pipe network water quality sudden change detection system and method. Comprising a multi-parameter distributed sensing network module, a data transmission module, a data processing and analyzing module, a space-time traceability positioning module and a user interaction and alarm module, wherein the multi-parameter distributed sensing network module comprises various types of water quality sensors and hydraulic sensors; the data acquisition module is used for acquiring water quality parameter data and hydraulic parameter data of each node in real time; the invention relates to the technical field of water quality monitoring. According to the underground pipe network water quality abrupt change detection system and method, multi-dimensional data are collected in real time through a distributed anti-interference sensing network, and an intelligent closed loop from data collection to abrupt change recognition is constructed in combination with'mixed transmission architecture + improved Bayesian network fusion analysis + LSTM-AE deep learning detection '; a complex sudden change mode of multi-parameter cooperative change can be rapidly identified, and risk expansion caused by monitoring lag or shallow layer analysis is avoided.

The invention discloses a psychological state dynamic evaluation and early warning system based on multi-modal behavior data, which belongs to the field of medical care informatics and comprises a multi-modal behavior data hierarchical coding module, a time sequence causal atlas construction and reasoning module, a double-stage self-adaptive early warning decision module and a context awareness intervention strategy generation module. A cross-modal association mode is extracted through a double-layer coding mechanism, a time sequence graph containing a causal relationship is constructed, causal reasoning is performed, a double-stage mechanism of short-term mutation detection and long-term trend prediction is adopted to generate graded early warning, and an optimal intervention strategy is selected based on a deep Q network according to a user situation. According to the method, the accuracy, timeliness and intervention effectiveness of psychological health assessment are improved, and dynamic monitoring and early warning of the psychological state are realized.

The invention belongs to the technical field of visual identification, and particularly discloses a thick plate saddle-shaped weld groove feature extraction and size measurement method and system. Comprising the steps that based on multi-scale geometric feature mutation detection, a current frame weld groove three-dimensional point cloud is segmented into a branch pipe face, a groove side wall, a backing weld surface and a main pipe face, and four area three-dimensional point clouds are obtained; according to the three-dimensional point cloud of each area, determining an intersection point of a branch pipe surface and a groove side wall, an intersection point of the groove side wall and a backing welding surface, an intersection point of the backing welding surface and a main pipe surface, an intersection point of the branch pipe surface and the main pipe surface, and a virtual intersection point of the groove side wall and an extension line of the main pipe surface as weld groove feature points; and constructing vectors between the feature points, and calculating the geometric dimension of the weld groove through vector operation. The method is based on multi-scale geometric feature mutation detection and adaptive point cloud segmentation, a fitting curve slope threshold value does not need to be preset, and the method can be adaptive to different included angles between the branch pipes and the main pipe in the pipe tubular joint.

The invention relates to the technical field of bioinformatics, and discloses a method, a system, equipment and a medium for searching for base mutation aiming at third-generation full-length transcript sequencing data, the base mutation is accurately positioned to a specific transcript by directly processing the third-generation full-length transcript sequencing data, and the method and the system for searching for the base mutation aiming at the third-generation full-length transcript sequencing data are provided. The co-occurrence relation of a plurality of mutations on the same transcript is accurately analyzed, and the defects of calculation redundancy, function misjudgment and the like caused by the fact that a mutation transcript source cannot be determined due to fragmentation splicing and the co-occurrence is simulated by depending on permutation and combination in a second-generation short-read-long sequencing technology are effectively overcome; the recall rate of transcripts which are not mapped due to a high-variation region is improved through mapping correction assisted by structural variation, and false positive is effectively inhibited through a multi-dimensional filtering condition, so that the sensitivity and reliability of mutation detection are remarkably improved; particularly, mutation events with function remodeling due to reading frame change can be accurately recognized in scenes such as neoantigen prediction where mutation function consequences need to be accurately evaluated, and the method has important value in application in the fields of precision medical treatment and the like.

The invention discloses a current abrupt change identification method based on comparative learning. The method comprises the following steps: S1, collecting three-phase current time sequence data and generating a sample; s2, performing normalization and noise suppression on the sample to obtain a standardized time sequence vector; s3, constructing positive and negative sample pairs based on the current change trend for comparative learning; s4, inputting the sample pair into the multiple perceptual hash network to generate a fusion vector; s5, through channel difference calculation and residual fusion, extracting mutation features; s6, introducing a time position coding gating mechanism, and dynamically adjusting the channel weight; s7, optimizing a model structure and parameters by adopting an improved black hole optimization algorithm; and S8, performing mutation detection based on similarity calculation and outputting a time position. According to the invention, high precision and high robustness of current abrupt change detection are realized, the power system monitoring efficiency is improved, and the method is suitable for intelligent fault diagnosis in a complex power environment.

The invention provides a typhoon path prediction method and system fusing physical constraint and path mutation recognition. The method comprises the steps that a multi-dimensional input feature tensor is obtained through a feature tensor generation module; performing time modeling through a multi-scale modeling module to obtain a deep feature tensor, and performing significance guidance through a significance guidance module to obtain a prediction path sequence; the bending event identification module identifies a bending angle to obtain a plurality of path abnormal scores, the adversarial disturbance analysis module introduces a plurality of preset disturbances to determine the path confidence, and the path prediction output module outputs a path prediction result. According to the technical scheme of the embodiment of the invention, refined modeling can be carried out on the typhoon path, the PINNs physical constraint module is utilized to ensure that the prediction result meets the physical conservation principle, the path mutation detection capability is enhanced through bending event recognition, uncertainty evaluation is realized through disturbance analysis, and the accuracy of the typhoon path detection is improved. And a typhoon path prediction result which is more accurate and credible and has physical consistency is output.

The invention relates to the technical field of biological medicine, in particular to an RNF / CLR / Cas / crRNA compound as well as a preparation method and application thereof. The RNF / CLR / Cas / crRNA compound disclosed by the invention is composed of an RNA nanoflower (RNF), a circular fluorescent reporter molecule (CLR) and a Cas / crRNA compound, and the circular fluorescent reporter molecule and the Cas / crRNA compound are combined on the RNA nanoflower through base complementary pairing; the Cas / crRNA compound is composed of a Cas protein and crRNA, and the crRNA is used for specifically recognizing the crRNA of a nucleic acid molecule. The RNF / CLR / Cas / crRNA compound can be used for nucleic acid molecule specificity detection and nucleic acid molecule base mutation detection, can also be used for fluorescence in situ hybridization tissue imaging, and has a good application prospect.

The invention belongs to the technical field of gene detection, and particularly relates to application of a CFTR gene mutation combination, an amplification reagent and a product in preparation of a CF risk assessment product. The CFTR gene mutation combination consists of an intron region mutation combination and an exon region mutation combination, based on the CFTR gene mutation combination, the invention further develops an amplification reagent and a CFTR gene mutation detection product, the amplification reagent and the CFTR gene mutation detection product comprise a primer combination for multiplex PCR amplification of the CFTR gene mutation combination, and the primer combination has the characteristics of high accuracy, high specificity and high sensitivity, can accurately detect related gene mutation, and provides a reliable basis for risk assessment and diagnosis of CF. When the amplification reagent and the CFTR gene mutation detection product are combined with sweat chlorine detection for use, the diagnosis rate of CF can be remarkably improved, and the amplification reagent has important application value in clinical diagnosis of CF and is expected to provide more powerful support for early diagnosis and treatment of CF patients.

The invention discloses a complex stratum dual-mode shield construction rock ballast particle size distribution evaluation method and system, and the method comprises the steps: obtaining a rock ballast image, carrying out the preprocessing and denoising, and obtaining a first image; utilizing a deep learning algorithm to identify the edge of the rock ballast, and separating overlapped rock ballast to obtain a second image; analyzing the shape of the rock ballast through morphological parameters, and performing three-dimensional reconstruction correction on the non-spherical particles to obtain a third image; performing data fusion on the third image in combination with laser particle size analysis data to obtain initial particle size distribution; aiming at a fine particle adhesion phenomenon, correcting particle size distribution by adopting a detection algorithm; dynamically updating particle size distribution according to the change of a crushing mechanism during shield mode switching; non-linear mutation in the complex stratum is analyzed through a mutation detection algorithm, and data smoothing is carried out; and finally, analyzing the error by using an error accumulation model, and if the error exceeds a threshold value, reprocessing until the error meets the requirement. According to the method, the precision and reliability of rock ballast particle size distribution evaluation are remarkably improved.

The invention relates to a pig abnormal behavior recognition method based on a three-axis attitude angle, and belongs to the technical field of intelligent breeding and animal behavior monitoring. The method comprises the following steps: acquiring three-axis attitude angle data of a pig, wherein the three-axis attitude angle data comprises a yaw angle, a pitch angle and a roll angle; performing time sequence processing, frequency domain analysis, statistical feature extraction or state sequence modeling on the attitude angle data; and constructing a behavior recognition model, and judging whether an abnormal behavior exists or not. The behavior recognition model may include weighted scoring functions, spectral energy analysis, Z-score mutation detection, or behavior chain modeling based on state transition diagrams. According to the method, multi-dimensional fusion of identification logic is realized, the method is suitable for monitoring various abnormal behaviors of pigs such as oestrus, parturient, fright, rolling and manic-depressive pigs, has the characteristics of low power consumption, simple equipment, extensible algorithm and the like, and is suitable for being deployed in a wearable intelligent terminal of the pigs.

The invention provides a time sequence data mutation detection method and system based on multi-stage dynamic optimization and a storage medium, and belongs to the field of radiotherapy equipment state monitoring and quality control. The invention provides the equipment state change point detection method which does not need penalty parameters and is based on'multi-stage dynamic optimization '. According to the method, on the basis of Dynp and RBF models in a ruptures tool library, the accuracy and stability of change point detection are effectively improved through candidate change point supplementation, local refinement, redundant change point deletion and other stage processing. The method can be used for detecting time sequence data mutation related to quality control in the radiotherapy equipment, and the stability and safety of the radiotherapy equipment in the using process are guaranteed.

This invention provides a germline mutation detection method and system with a low false positive rate. The system is computer-executed and includes: first, performing a PCR repeat cluster consistency test on sequence alignment files generated from high-throughput sequencing reads, down-regulating the base count weights of inconsistent sites within the cluster; then, based on the sample-specific background error baseline, calculating the variation confidence index of each genomic site using an empirical Bayesian framework to obtain candidate single nucleotide variants (SNPs); obtaining candidate insertion / deletion variants through read clustering and physical verification of insertion fragment lengths; subsequently, performing a dual-engine cross-feedback iteration on the two candidate types until convergence, integrating and filtering, and outputting a structured mutation detection report. This invention significantly reduces the false positive rate of both SNPs and insertion / deletion variants while maintaining sensitivity, and improves the detection capability of complex insertion / deletion variants.

The invention relates to an asynchronous federated learning method and system, and the method comprises the steps: firstly, obtaining the prediction training time of a client through employing an exponential smoothing method in combination with anomaly detection and mutation detection according to the federated learning reality conditions of data isomerism, system isomerism and the like; secondly, utilizing a dynamic threshold segmentation algorithm and Monte Carlo simulation to obtain a two-stage waiting time threshold; and finally, the server selects a local model uploaded by the client to perform parameter aggregation in combination with the training time predicted by the client and a two-stage waiting time threshold, and finally forms a self-adaptive federated learning resource scheduling strategy. According to the method, client node resources can be used to the maximum extent at low cost, the global model training efficiency and precision are improved, and the multi-scene adaptability of federal learning is effectively enhanced. Experimental results show that compared with a classical method, the method has higher accuracy.

The invention discloses a bladder cancer mutation detection and typing method and system using pathomics characteristics, and relates to the technical field of disease detection.The bladder cancer mutation detection and typing method comprises the steps that related data of bladder cancer tissue of a patient is received, and then a bladder cancer tissue pathological section image is subjected to image segmentation to obtain a segmented bladder cancer tissue pathological section image; feature extraction processing is carried out based on the segmented bladder cancer tissue pathological section image to obtain bladder cancer tissue pathological feature data, feature weight weighting processing is carried out based on the bladder cancer tissue pathological feature data to obtain a bladder cancer comprehensive weight coefficient, and the bladder cancer comprehensive weight coefficient is predicted through an ecDNA probability prediction model to obtain an ecDNA probability coefficient; and performing multi-modal space fusion on the ecDNA probability coefficient and bladder cancer tissue gene data to obtain an ecDNA risk score, and finally realizing bladder cancer mutation detection and typing based on the ecDNA risk score.

The application discloses a kind of monitoring variable frequency sampling method, equipment, system and storage medium, the method comprises: obtaining the first sampling data to be detected, first sampling data is that monitoring proxy end is sampled to monitoring object according to set first sampling time interval and obtains first sampling data;According to the operation state of monitoring object, mutation detection is carried out to first sampling data, and mutation detection result is obtained;If it is determined that mutation detection result is mutation, the second sampling data before mutation is obtained, and the second sampling data and first sampling data are sent to monitoring server;Wherein, second sampling data is that monitoring proxy end is sampled in first set time before mutation according to set first sampling time interval and obtains first sampling data, and first set time is determined according to set first sampling data queue length.Therefore, the real-time performance and accuracy of mutation analysis are improved.

A system and method for identifying working medium solidification of a liquid metal heat exchanger comprises a monitoring system, the monitoring system is installed on the liquid metal heat exchanger, temperature and pressure difference information of a working medium inlet and a working medium outlet of the liquid metal heat exchanger is obtained through the monitoring system, and therefore the working medium solidification condition of the liquid metal heat exchanger is confirmed. According to the method, the physical influence of the solidification phenomenon on the heat exchange process in the heat exchanger is combined, multi-area working medium parameters are measured, and judgment logic combining moving average, trend judgment, trend variation analysis and mutation detection is adopted, so that solidification occurrence identification based on multi-physical-parameter dynamic coupling is achieved. And meanwhile, the sudden change detection is adopted, so that the embodiment can respond to the serious rapid solidification phenomenon more rapidly, and the operation safety of the heat exchanger is further improved. According to the identification method, the operation state of the heat exchanger can be monitored in real time, the solidification phenomenon can be captured earlier, and the sensitivity and stability of early warning are both considered.

The application relates to the technical field of gene sequencing data processing and bioinformation analysis, in particular to a Web-based whole-exome sequencing tumor gene mutation detection visual analysis method and system. The system collects user sequencing data and a reference genome version through a Web interactive interface; a program is called to perform quality control cleaning and evaluation on the data, and a visual report is generated; sequence alignment is completed based on the reference genome, and a variation site is identified through algorithm iteration; biological annotation of the variation is combined with a database, a candidate pathogenic mutation set is screened out in multiple levels according to a strategy, the candidate set is projected to a visual interface, a site state is confirmed or removed in response to a manual checking instruction, and a final gene mutation detection report is generated. The application greatly simplifies the whole-exome sequencing data processing procedure, makes it easy for clinical doctors or researchers without bioinformation background to start, and improves the popularization rate and work efficiency of tumor gene detection work.

The invention discloses a detection kit for detecting JAK2V617F mutation, which is characterized by comprising: a JAK2V617F detection reagent, which comprises an inner primer pair, an outer primer pair and a specific probe for specifically amplifying a JAK2V617F mutant gene; the internal reference detection reagent comprises a primer pair and a probe of an internal reference gene; the PCR reaction buffer solution comprises a reaction mixed solution and a HotstartTaq enzyme; the kit comprises a positive control, a known proportion of V617F mutant DNA, a negative control, a JAK2V617F wild type DNA, a positive control and a known proportion of V617F mutant DNA. Compared with the prior art, the invention has the advantage of providing the detection kit for detecting JAK2V617F mutation, which can be used for diagnosis, identification and clinical prognosis of PV.

The invention provides an ear tag system for cow behavior recognition based on a three-axis attitude angle, and relates to the technical field of intelligent health management and behavior monitoring of cows such as cows, beef cattle and buffalo. The system comprises an intelligent terminal device which can be worn on the ear of a cattle, and a gyroscope sensor is integrated in the device and used for collecting three-axis attitude angle data (a yaw angle Yaw, a pitch angle Pitch and a roll angle Roll) of the cattle in real time; and the behavior identification module is used for identifying behaviors of the cattle based on the attitude angle signal, such as rumination, ingestion, lying, pacing, repeated rising and lying, oestrus jumping and riding and the like. The recognition module comprises a weighted scoring mechanism, a frequency domain feature extraction algorithm, a mutation detection model, a behavior state transition modeling and other mechanisms, and can be deployed on an ear tag terminal or a remote platform to execute analysis. The system has the advantages of being convenient to deploy, accurate in recognition, low in power consumption, easy to integrate and the like, and is suitable for large-scale intelligent cattle breeding and remote health supervision.

The application discloses a method for detecting glioma chromosome abnormalities based on targeted sequencing, and belongs to the technical field of biological medicine. The method first acquires the allele frequency of a to-be-detected sample at preset SNP sites (covering 1p, 1q, 19p, 19q, chromosome 7 and chromosome 10), and then calculates and determines whether specific chromosome arms or chromosomes have loss of heterozygosity. Meanwhile, the copy number of the region where each SNP site is located is calculated based on the sequencing depth, and the total copy number of the above-mentioned chromosomes is obtained by integration. Finally, the loss of heterozygosity determination result and the chromosome copy number information are comprehensively combined, so that the simultaneous identification of 1p / 19q co-deletion, gain of chromosome 7 (+7) and deletion of chromosome 10 (-10) is realized. The method does not require paired samples, can accurately quantify the copy number, avoid false positives, and only needs to detect part of the SNP sites, that is, can be combined with hot spot mutation detection, thereby saving cost and improving detection efficiency.

The present application relates to the technical field of gene mutation detection, and particularly relates to a system, method and application for detecting exon skipping mutation.The present application constructs a self-catalytic system based on specific double recognition induction of CRISPR-Cas12a, which is used for sensitive and reliable detection of MET 14 exon skipping mutation of lung cancer patients.The system can effectively release the fluorescence signal through the Cas12a auxiliary cutting, and further realize signal amplification through the self-catalytic mechanism.The present application can solve the technical problems of the existing exon skipping mutation detection system, such as complex detection process, long time consumption, and unsatisfactory detection accuracy.The detection system of the present application has high sensitivity, stability and good reproducibility, and the method has low cost, simple operation, short detection period, good specificity, and low false positive rate and false negative rate.The present application is suitable for clinical sample determination and has an ideal application prospect.

The application provides a primer probe set and a kit for detecting a rifampicin-resistant gene of Mycobacterium tuberculosis, and belongs to the technical field of molecular biology detection.The primer probe set comprises RAA primers and qPCR primers targeting the rpoB gene, probe primers targeting the wild-type rpoB gene, and probe primers targeting the mutant rpoB gene.The application adopts the detection methods of RT-RAA and qPCR in sequence, and the above specific primer probe set can be combined to simultaneously and rapidly detect the 516, 526, 531 and 533 sites of the rpoB gene of Mycobacterium tuberculosis, has good specificity, and the detection sensitivity can reach 5 copies / ul, is 20 times higher than the sensitivity of qPCR (100 copies / ul), and has a 5% detection capability for heterogenic drug resistance mutation; the detection method can also greatly shorten the detection time.

The invention relates to an AKR1C3 enzyme expression level and medical application through KRAS mutation. According to the AKR1C3 enzyme activated anticancer prodrug, KRAS gene mutation can be directly used as a detection target before medication, namely, a KRAS mutation patient is a patient with high expression of the AKR1C3 enzyme and does not need to be subjected to AKR1C3 enzyme expression level or AKR1C3RNA detection, that is to say, KRAS mutation detection positive can be used as a screening index to screen the patient with high expression of the AKR1C3 enzyme.