1598 results about "Quantitative assay" patented technology

The invention discloses a flotation reagent formula optimization method and system based on data analysis, and the method comprises the steps: monitoring the ore pulp of a flotation tank in real time through an online detection device, obtaining a mineral grade, reagent concentration and foam thickness data set, processing the data set based on a time-delay analysis algorithm, generating a reagent association feature library, and carrying out the optimization of the flotation reagent formula. And clustering the feature library according to medicament types to construct an effect association map, calculating the map by using a multi-objective optimization algorithm to generate a candidate formula set, screening and determining an optimal formula according to cost constraint, and controlling a medicament adding device to execute adjustment. According to the method, the problem that an existing flotation reagent formula optimization technology is lack of reagent synergistic effect quantitative analysis and time sequence correlation modeling is solved, and the scientificity and accuracy of reagent formula optimization are improved.

The invention relates to the technical field of spectral analysis and concentration quantitative detection, and discloses an LIBS quantitative analysis method, device and system based on a thin plate spline regression algorithm. The method comprises the steps that multiple sets of sample data are obtained, and each set of sample data comprises corresponding target sample concentration, laser energy and spectral intensity; constructing a concentration quantitative model about the concentration, the laser energy and the spectral intensity based on a spline function; determining an optimal penalty parameter by adopting a cross validation method; based on the optimal penalty parameter, solving parameters of the concentration quantitative model through a minimization objective function to obtain a constructed concentration quantitative model; and performing quantitative analysis on a sample with unknown concentration by using the constructed concentration quantitative model. Thus, the output energy of the laser is incorporated into the core model, and an I-E-C nonlinear coupling relationship is constructed, so that the problem of calibration deviation caused by laser energy fluctuation is reduced, and the precision improvement of concentration quantification in a complex scene is realized.

The invention relates to the technical field of spectrum detection, in particular to an element quantitative analysis method, system and equipment and a medium. The method comprises the following steps: acquiring LIBS spectral data of a sample; preprocessing the LIBS spectral data to obtain target spectral data; according to the target spectral data, on the basis of an element spectral line standard library, the analysis element and the matrix element are calibrated, and a calibrated analysis element spectral line and a calibrated matrix element spectral line are obtained; according to the spectral line of the analysis element and the spectral line of the matrix element, calculating to obtain the plasma temperature of the analysis element by using a Boltzmann planar graph method; and inputting the analysis element plasma temperature and the analysis element spectral line into the element quantitative analysis model to obtain the predicted content of the analysis element. The method effectively solves the problems of low detection efficiency, complex operation, sensitive matrix effect and the like in the prior art, and is suitable for rapid and accurate quantitative analysis of stainless steel elements on an industrial site.

The invention provides a rapid quantitative analysis method for an outer vesicle proteome, and belongs to the technical field of quantitative analysis of the outer vesicle proteome. According to the rapid quantitative analysis method for the outer vesicle proteome, firstly, a high-quality outer vesicle sample is obtained through low-speed centrifugation, ultra-speed centrifugation and membrane filtration; and obtaining proteome data by utilizing two-dimensional electrophoresis and mass spectrometry. Then, a mathematical model equation set is constructed for preliminary analysis, a protein interaction network is optimized through an embedded game optimization index model, and feature extraction and quantitative analysis are conducted through a convolution parameter adaptive function and a residual network deep learning model; finally, a quantitative analysis result containing the protein expression level and the function importance is generated in combination with the multi-dimensional protein expression contribution index, and the technical problem that in the prior art, in the rapid quantitative analysis process of the outer vesicle proteome, the protein component complexity is high, and consequently the quantitative precision is low is solved.

The invention relates to the technical field of data compensation, and discloses an intelligent compensation method for dynamic response delay of a sensor, which comprises the following steps: judging whether a target sensor has dynamic response delay, collecting regional data, analyzing the distribution condition of the sensors in a regional set, judging whether the dynamic response delay is related to the environment, and if yes, judging whether the dynamic response delay is related to the environment. According to the intelligent compensation method for the dynamic response delay of the sensor, the efficiency of the whole process is improved through a batch processing and efficient data analysis method, analysis and compensation of a large number of sensors can be completed in a short time, and the intelligent compensation method for the dynamic response delay of the sensor is suitable for large-scale popularization and application. Through quantitative analysis and accurate environmental impact assessment, the accuracy and reliability of sensor measurement are improved, and the overall performance of the system is enhanced.

The invention relates to an overlapped chromatographic peak positioning and splitting method and system based on a numerical stable function. The method comprises the following steps: de-noising chromatographic data and correcting a baseline; calculating a second derivative by using SG filtering, grouping continuous points according to a negative threshold value, and determining an initial position of each peak by using a second-order minimum value; establishing a peak function of bidirectional exponential correction by taking a peak position as an initial value, setting parameters and boundaries such as area, center, width, trailing and the like, constructing a residual error minimization target, and performing robust fitting by adopting a trust region reflection algorithm; and indexes such as suitability, output peak area, center, broadening, separation degree, theoretical plate number and the like are verified by residual errors. According to the method, valley point or prior component information is not needed, the method is insensitive to noise and baseline drift, serious overlapping and trailing / leading edge peaks of multiple components can be effectively processed, convergence is high, the method is insensitive to initial values, parameters can be expanded to different peak shapes, and the method is suitable for qualitative and quantitative analysis of automatic and high-flux chromatographic data.

The invention belongs to the technical field of medical diagnosis, and discloses a quantum multicolor immune digital pathological diagnosis analysis system, which comprises the following steps: acquiring high-resolution digital images of an immunohistochemical or immunofluorescence slice and a slide, and analyzing multicolor signal distribution in the images to obtain a quantum chromatographic image set; pixel-level color separation and intensity quantization are carried out on the quantum chromatographic image set, and a quantum intensity index set of each pixel is generated; performing multi-target color overlay analysis and post-translational modification protein specificity evaluation on the quantum intensity index set to construct a multi-color modification index set; identifying a single cell boundary and a multi-cell community based on the multicolor modification index set, and extracting a cell morphological parameter and a spatial adjacency relation to obtain a cell quantum analysis set; performing consistency correction and traceable recording by applying an automatic quality control mechanism, and generating a digital diagnosis report; the accuracy of accurate qualitative and quantitative analysis of immunohistochemistry and immunofluorescence is greatly improved.

The invention relates to the technical field of sewage / wastewater denitrification, in particular to a method for predicting and explaining denitrification performance and microbial abundance of a single-stage PNA system, which comprises the following steps: constructing an ML model of nitrogen removal rate and functional microbial abundance, and incorporating key water quality and operating parameters into the model to improve the interpretability and prediction accuracy of the model; and the influence of each input feature on the model and the optimal range of the environmental parameters are determined, so that targeted operation adjustment is realized. Quantitative analysis is carried out through the model, potential synergistic effects among the characteristics can be explored, and therefore the optimal characteristic range can be found, the wastewater treatment efficiency can be improved, and the optimal water environment can be obtained.

The invention constructs a universal visual virus molecularly imprinted sensor modified with an anti-protein coating, the sensor uses magnetic Fe3O4 as an imprinting carrier, HBV as a template and dopamine as a functional monomer for imprinting to obtain a molecularly imprinted polymer with a polydopamine coating (PDA), and before the template is removed, the molecular imprinting polymer with the polydopamine coating (PDA) is separated from the template, so that the molecular imprinting polymer with the polydopamine coating (PDA) is obtained. The surface of the PDA layer is covered with a slightly cross-linked PMEO2MA layer through a water precipitation polymerization method, and the polymer can effectively reduce non-specific protein adsorption; and finally, exposing an imprinting cavity of the PDA layer by eluting the template molecules. On the other hand, potassium permanganate (KMnO4) is used as a signal output molecule, and when no target object exists, the strong reducibility of PDA can enable KMnO4 entering the imprinting cavity to be subjected to a color fading reaction. Based on the principle, high-specificity colorimetric visual detection on the HBV is realized. The sensor can be combined with a smart phone through naked eyes to carry out visual semi-quantitative analysis and carry out quantitative detection through ultraviolet rays, and the universality of the sensor can be confirmed through a template replacement experiment. And meanwhile, the sensor shows an excellent cost advantage. The diagnosis which is high in sensitivity, low in cost and capable of achieving rapid visualization shows huge potential in practical application, and particularly has important application value in infectious disease screening and monitoring work in areas with limited resources.

The invention discloses a method for determining volatile organic compounds in surface water by purging and trapping and gas chromatography-mass spectrometry, which comprises the following steps: acquiring mass spectrometry data of volatile organic compounds in a surface water sample, and separating and detecting a multi-component mixture in a complex matrix by gas chromatography-mass spectrometry, obtaining an original data set containing retention time and mass spectrum information; according to an output result of the classification model, carrying out qualitative analysis on unknown compounds in a to-be-detected sample, and if the classification probability is greater than a preset threshold value, determining the types of the compounds to obtain an accurate qualitative recognition result; and establishing a concentration standard curve by adopting a corresponding quantitative ion peak area according to a target compound confirmed in the qualitative identification result, and calculating a linear relation parameter between the peak area and the concentration to obtain a regression equation for quantitative analysis.

This invention discloses a spectral preprocessing method and related equipment for online coal quality detection using multispectral coupling. The method includes: simultaneously acquiring LIBS and NIRS spectra of raw coal using an online coal quality detection device; screening the effectiveness of the synchronized LIBS and NIRS spectra; identifying the defocusing amount of each set of synchronized LIBS and NIRS spectra and assigning a corresponding defocusing amount label to each set of spectra; correcting the defocused spectra according to the defocusing amount label using a spectral correction method to obtain corrected LIBS and NIRS spectra, and then fusing them to obtain a corrected fused spectrum; analyzing the LIBS spectrum, NIRS spectrum, and fused spectrum using quantitative analysis methods, and calculating the fixed carbon content. This invention's LIBS and NIRS spectral fusion overcomes the limitation of a single spectrum only characterizing atomic or molecular information, enabling a complete characterization of the chemical information of the coal sample and achieving simultaneous and accurate analysis of multiple coal quality indicators.

The invention discloses an absolute quantitative correction method and device for medical imaging equipment, and relates to the technical field of nuclear medical imaging equipment correction. When the method is executed, the actual activity concentration of the calibration die body injected with the first known tracer activity serves as the first activity concentration; then, the calibration die body injected with the first known tracer activity is collected in medical imaging equipment, and the activity concentration in a first image domain obtained through calculation serves as second activity concentration; and finally, calculating a correction factor based on the first activity concentration and the second activity concentration, wherein the correction factor is used for correcting absolute quantification of the medical imaging equipment. Therefore, the correction factor is calculated by comparing the activity concentration calculated by the medical imaging equipment with the actual activity concentration of the calibration die body, so that the medical imaging equipment can accurately adjust the measurement result according to the correction factor during subsequent absolute quantitative analysis; the effect of improving the absolute quantification accuracy of the medical imaging equipment is achieved.

The invention discloses a substance-based quality detection method for Xiaohuang powder, and belongs to the technical field of quality detection of traditional Chinese medicines. According to the method, standard control of a classic famous prescription is realized by constructing a multi-dimensional quality control system. The method comprises the following steps: firstly, carrying out specific identification on gardenia, divaricate saposhnikovia root, liquorice and pogostemon cablin in the prescription by adopting a thin-layer chromatography; meanwhile, a multi-index component content determination method is established, and the contents of five components including geniposide, cimicifugin, 5-O-methylvisammioside, liquiritin and glycyrrhizic acid are synchronously determined by applying a high performance liquid chromatography (HPLC). Through a system quality control strategy combining qualitative identification and quantitative analysis, not only is a quality standard system of the substance reference of the Xiaohuang powder perfected, but also reliable technical guarantee is provided for ensuring the stability and consistency of product quality. The establishment of the method establishes a standardized method system for the substance-based quality control of the classical famous prescription Xiaohuang powder.

The invention belongs to the technical field of biology, and particularly relates to application of hepcidin expression quantity in larimichthys crocea liver as larimichthys crocea visceral white-spot disease resistance evaluation index and a construction method. The hepcidin expression quantity in the larimichthys crocea liver is applied as the larimichthys crocea visceral white-spot disease resistance evaluation index. The construction method of the resistance evaluation index comprises the following steps: taking healthy small yellow croaker intraperitoneal injection pseudomonas proteinus strain bacterial liquid, and determining a half lethal dose in 96 hours; the method comprises the following steps: injecting a half lethal dose of bacterial liquid of pseudomonas proteinus strains into the abdominal cavity of healthy small yellow croaker for 96 hours, and after infection, taking different tissues to carry out hepcidin gene expression fluorescent quantitative PCR analysis; a regression model of gene expression quantity and bacterium loading quantity is established through fluorescence quantification, and the relationship between hepcidin expression quantity and disease resistance is constructed. Accurate disease-resistant phenotype information is provided for development of breeding of visceral white-spot disease-resistant improved varieties of the small yellow croakers, the accuracy of disease-resistant breeding is promoted, early detection of diseases is realized, and prevention and treatment of the diseases are effectively guided.

The invention belongs to the technical field of microbiological detection, and particularly relates to a primer and a probe for detecting amylooosperm whipstock and application of the primer and the probe. In allusion to the SSSUrRNA gene of the amylooosperms, the invention designs the real-time fluorescent quantitative PCR detection primer and probe based on the probe method, the detection sensitivity of the real-time fluorescent quantitative PCR detection primer and probe to the target gene is 6.60 copies / reaction, and the real-time fluorescent quantitative PCR detection primer and probe have the advantages of rapid detection and strong specificity, and can be used for the detection and quantitative analysis of the amylooosperms of non-disease diagnosis and treatment purposes.

The invention relates to a method for quantitatively analyzing the evolution period of paleo-pore fluid in a sandstone reservoir, and belongs to the technical field of oil and gas reservoir exploration and development. According to the method for quantitatively analyzing the evolutionary period of the paleo-pore fluid in the sandstone reservoir, core samples with different oiliness are selected at a target position in a target interval of a research area, fluid activity periods are preliminarily analyzed, and then the core sample with the most fluid activity periods is selected as a standard sample; pore fluid evolution analysis can be carried out on a standard sample, the analysis process is shortened, and the precision of recovering pore fluid evolution is improved.

The invention provides a method for detecting fluorine compounds in shrimps, which comprises the following steps: S1, sample preparation: removing shells of to-be-detected shrimps, and crushing and homogenizing muscular tissues of the to-be-detected shrimps to obtain a to-be-detected sample; s2, extraction: extracting a sample to be detected, retaining an organic phase, and concentrating to obtain a concentrated solution; then adding water which is 5-15 times of the concentrated solution for dilution to obtain a to-be-purified solution; the extraction solvent is an aqueous solution of an organic solvent; s3, purification: purifying the to-be-purified liquid by using a solid-phase extraction column to obtain a purified liquid; s4, concentration and constant volume: concentrating the purified liquid to be nearly dry, redissolving, and filtering to obtain filtrate; and S5, sample detection: detecting the filtrate by using a liquid chromatograph-mass spectrometer, and carrying out qualitative and quantitative analysis on the fluorine compounds in the prawns by adopting a standard curve method. The method can be used for simultaneously determining the contents of 41 PFASs compounds with different physicochemical properties in shrimps.

The invention provides a quantitative analysis method for red mud reaction degree, which comprises the following steps: preparing a cementing material sample containing red mud, acquiring a back scattered electron (BSE) image of the sample, performing semantic segmentation on the BSE image by using a deep learning model, identifying a red mud correlation phase, and determining the red mud reaction degree on the basis of a semantic segmentation result. Calculating the area fraction of the red mud related phase in the image, converting the area fraction into the volume fraction of the red mud, and calculating the reaction degree of the red mud based on the volume fraction; the invention aims to overcome the limitations of strong indirectly, low precision and high cost in the prior art, realizes direct, objective and visual quantification of the red mud reaction degree by accurately identifying the related phase of the red mud and establishing the area-volume equivalent relationship, and provides a reliable technical means for optimizing the red mud composite cementing material.

The invention belongs to the technical field of iron ion detection, and particularly relates to a rapid detection method for ferric ions in water, which comprises the following steps: a fluorescent probe preparation step: taking trihydrate of copper nitrate and tryptamine, adding into a centrifuge tube in sequence, adding ultrapure water, performing ultrasonic dissolution, transferring into a high-pressure reaction kettle, and performing hydrothermal reaction to obtain a fluorescent probe; cooling to room temperature, centrifuging the obtained yellow green solution in a centrifugal machine, and filtering the supernate through a microfiltration membrane to obtain a copper-doped carbon quantum dot CuCDs stock solution, namely the fluorescent probe; analyzing and determining Fe < 3 + >: taking the diluent of the prepared CuCDs solution, adding the diluent into a centrifugal tube containing a Fe < 3 + > standard solution, uniformly mixing, carrying out fluorescent quantitative analysis and detection, and exploring the change rule of the fluorescence intensity ratio of CuCDs; according to the rapid detection method for Fe < 3 + > provided by the invention, the synthesized CuCDs have high sensitivity, can realize on-site rapid detection of Fe < 3 + > in a water medium, and shows a wide application prospect in the field of environmental monitoring.

The invention provides a tissue microvessel analysis method and system for assisting drug design based on artificial intelligence. The method comprises the steps that two channel images of a blood vessel marking channel and a nano probe channel are collected; carrying out preprocessing, registration and feature fusion on the two channel images to obtain a fusion feature tensor for simultaneously coding a blood vessel-nanoparticle space coupling relationship; extracting multi-scale structure information based on the fusion feature tensor to obtain an enhanced feature tensor; constructing double models based on a U-Net + + architecture, and combining the double models with an enhanced feature tensor to obtain a blood vessel mask, a nanoprobe mask and a preprocessed probe intensity graph; and obtaining a permeability index based on the blood vessel mask, the nano probe mask and the preprocessed probe intensity graph, and obtaining a blood vessel typing and drug recommendation design strategy by adopting a clustering and random forest regression algorithm. According to the method, the quantitative analysis precision and efficiency of the vascular permeability are remarkably improved, intelligent linkage of image analysis and a nano-drug design strategy is realized for the first time, and the method has good application potential.

The invention discloses a method for simulating a spreading rule of African swine fever virus aerosol in a pig farm, and relates to the technical field of animal epidemic disease spreading simulation. Comprising the following steps: selecting fluorescent microspheres, and preparing a fluorescent microsphere suspension; building a fluorescent microsphere suspension release system; dividing a pig farm into a plurality of cells, and arranging a sampling network in the pig farm; starting all the samplers of the sampling network, idling for a certain time, and closing all the samplers after measuring the environment background concentration; synchronously starting a fluorescent microsphere suspension release system and samplers of all sampling networks, and synchronously recording environment temperature and humidity, wind speed and wind direction; and performing quantitative analysis on the fluorescence of the pig farm at different time points, and generating a distribution diagram and a dynamic change diagram of the fluorescence in the pig farm according to an analysis result. According to the invention, by releasing the fluorescent microspheres with matched particle sizes and combining with a gridding monitoring network, a distribution diagram and a dynamic change diagram during virus diffusion are dynamically generated, and data support is provided for pig farm ventilation optimization and accurate disinfection.

The invention relates to the technical field of microfluidics, in particular to a microfluidic isothermal amplification method and system based on multichannel spectrum detection, and the method comprises the following steps: adding a sample and a reaction premixed solution into a microfluidic chip to form a reaction liquid; at least two fluorescent probes are added into the reaction premixed solution; carrying out isothermal amplification reaction on the reaction liquid, and collecting spectrum time sequence data in the reaction process; and performing spectral deconvolution on the spectral time sequence data to respectively obtain an independent amplification curve corresponding to each fluorescent probe, and performing quantitative analysis. Aiming at the problem that a reaction amplification quantitative system in the prior art depends on a high-precision quantitative PCR instrument and an optical assembly and is relatively high in cost, a spectral deconvolution mode is introduced to separate independent amplification curves of all fluorescent probes on a calculation level, so that an expensive PCR instrument is omitted, and the cost is reduced. And the requirements on color filtering and detection precision of the optical assembly are reduced, and the method can be applied to detection scenes with relatively wide wave bands.

The invention discloses a YOLO-Drop-based 3D printing droplet microfluidic immunoassay method, which organically integrates a high-precision microfluidic chip preparation technology, a droplet-level immunoreaction system and a fluorescence imaging acquisition and deep learning detection algorithm. And full-chain closed-loop processing from droplet generation, fluorescence signal acquisition, image recognition, result statistics to concentration quantitative analysis is realized. Through deep combination of chip physical parameters and model input features, the method not only significantly improves the accuracy and stability of droplet identification and concentration quantification, but also enhances the robustness of the algorithm under complex sample conditions through multi-scale feature fusion and a signal dual screening mechanism. Meanwhile, the optimized YOLO-Drop model supports embedded efficient reasoning, so that the whole analysis process has the characteristics of real-time performance and high throughput, and the method is suitable for rapid quantitative detection of various biomarkers.

The invention belongs to the field of molecular sieve material characterization, and particularly relates to a method for analyzing aluminum atom pairs in a ZSM-5 molecular sieve based on super-resolution fluorescence microscopic imaging, which comprises the following steps: activating a ZSM-5 molecular sieve sample; contacting the obtained sample with probe molecules; washing the ZSM-5 molecular sieve sample, and removing unbound or nonspecifically bound probe molecules; imaging the marked sample by using a super-resolution fluorescence microscope to obtain an image sequence containing a single fluorescent molecule emission event; processing the image sequence, determining a space coordinate of each fluorescence event, and reconstructing a super-resolution fluorescence image exceeding an optical diffraction limit; and based on the space coordinates of the fluorescent dots in the super-resolution fluorescence image, carrying out statistics to obtain the position, surface density or distribution information of the aluminum atom pairs in the ZSM-5 molecular sieve. According to the method disclosed by the invention, nanoscale spatial resolution, direct imaging and quantitative analysis of Al-pair in ZSM-5 (Zeolite Socony Mobil-5) can be realized.

The invention relates to the technical field of liquid chromatographic analysis, and discloses a data acquisition method and system of a liquid chromatograph. The method comprises the following steps: extracting chromatograms without solvent residues and with known solvent residues from a historical database, and training by using a self-encoding neural network and a convolutional neural network to generate a baseline reference model and a ghost peak early warning model; obtaining solvent parameters of the previous analysis method, and calculating the residual solvent concentration through residual solvent information; inputting the residual solvent concentration into the baseline reference model and the ghost peak early warning model, and calculating to obtain a baseline drift prediction correction value and a ghost peak prediction risk area; pre-correcting the chromatographic signal acquired in real time to obtain a purified chromatographic signal; performing qualitative and quantitative analysis on the purification signal according to dynamic allocation resources of the ghost peak risk area to generate final chromatographic data; the data interference of the mobile phase residue is effectively eliminated, and the accuracy of data acquisition is improved.

The invention discloses a self-driven diagnosis and treatment integrated microneedle array paper-based chip as well as a preparation method and application thereof, and belongs to the technical field of biomedicine. The microneedle array chip system comprises a function integration module which at least comprises a self-powered extraction unit, a sensing detection unit and a controlled drug delivery unit. According to the microneedle array paper-based chip, biomarkers in skin interstitial fluid are efficiently collected based on a self-powered thermoelectric chemical battery microneedle, PtPB (at) PEG (at) HA nanoparticles loaded on an immunochromatography test strip combination pad are triggered to be subjected to ring opening, a hybridization chain reaction is induced to generate DNA nano-enzyme, and TMB is catalyzed to develop color; the open-loop nanoparticles can be used for SERS cross detection of biomarkers, and can be used as a thermal initiator to activate the phase change microneedle under the irradiation of near-infrared light, so that drugs are released as required, and precise treatment is realized. Besides, objective quantitative analysis of color signals is realized by constructing a convolutional neural network model, and the system has the functions of naked eye screening, intelligent quantification and personalized treatment, and can improve the home management ability of early skin cancer.

The invention discloses a system for detecting and analyzing water-soluble heavy metals in an ecological system based on machine learning. The system is used for detecting water-soluble harmful heavy metal elements in atmospheric particulates. The system comprises a sampling preprocessing module, an electrochemical detection module, a machine learning analysis module and a data verification output module which are connected in sequence, the sampling pretreatment module is used for collecting PM2.5 and PM10 particles by adopting a quartz fiber filter membrane and carrying out dissolving and filtering pretreatment; the electrochemical detection module is used for heavy metal enrichment and detection; the machine learning analysis module adopts an improved CNN (Convolutional Neural Network) to automatically identify the volt-ampere spectrogram, and establishes a nonlinear mapping model of the relationship between the metal ion concentration and the peak current; and the data verification output module performs quantitative analysis by adopting a standard addition method, and compares and identifies the pollution source through a multi-element fingerprint feature database. According to the invention, eight heavy metal elements such as Cd, V, Cr, Ni, Se, As, Mn and Pb can be detected at the same time, the detection range is 0.1-1000 [mu] g / L, and intelligence and automation of heavy metal detection are realized.

The invention discloses a positive charge fluorescent nanoprobe targeting BCR-ABL fusion protein and application of the positive charge fluorescent nanoprobe in leukemia single cell drug resistance detection, and relates to the field of biological medicine. According to the invention, the surface of the nanoprobe is subjected to specific modification of a polyethylene glycol hydrophilic polymer chain-bridged targeting molecule, so that the functionalized fluorescent nanoprobe with leukemia subcellular oncogenic fusion protein targeting property is successfully constructed. The probe realizes efficient and accurate targeting of the BCR-ABL fusion protein by regulating a subcellular transport pathway, completes diagnosis and quantitative analysis of drug resistance of the leukemia single-cell BCR-ABL fusion protein by utilizing an endocytosis-transport-exocytosis process of cells, can more comprehensively reveal heterogeneity and drug resistance conditions of BCR-ABL positive cells, and has a good application prospect. And a new technical means is provided for accurate diagnosis and treatment of chronic myelogenous leukemia.

The invention discloses an electrochemical urine biopsy system for cathepsin B analysis and a detection method of the electrochemical urine biopsy system. The electrochemical urine biopsy system comprises gold nanoparticles AuNPs, a peptide-peptide nucleic acid PNA series probe and an electrochemical sensor. The peptide-peptide nucleic acid tandem probe is combined with gold nanoparticles through a mercaptan modified sequence to form a spherical nanostructure with the diameter of 15 nm, a polypeptide substrate sequence serves as a specific reaction substrate of cathepsin B, and PNA provides stability and a signal shielding function. The electrochemical sensor is based on a cutting enzyme-assisted DNAwalker mechanism, signal amplification is started through methylene blue MB release, and high-sensitivity detection of the released PNA probe is achieved. The system provided by the invention has the characteristics of high specificity, high sensitivity and rapid response, can realize quantitative analysis of cathepsin B through noninvasive urine biopsy, and provides a new technical means for early diagnosis and treatment monitoring of tumors.

The application discloses a kind of microscale single particle quantitative analysis method and system based on nano-silver signal enhancement, it is related to spectral analysis technical field, the method includes: using laser-induced breakdown spectroscopy system to the carbon black particle target sample is carried out spectral data acquisition, obtains target full spectrum data and is pretreated, and then obtains target spectral data matrix;Variable importance measurement algorithm and continuous projection algorithm are used to the variable in the target spectral data matrix is screened, and target variable is obtained;Target variable is input into carbon black particle metal content prediction model, and the content of metal element in carbon black particle target sample is obtained.The application can quickly and accurately determine the content of metal element in carbon black particle target sample.