227 results about "Cell analysis" patented technology

The invention discloses a cervical lesion intercellular relation modeling and analysis system based on a graph neural network, and the system comprises a medical image collection module which is used for collecting a digital image of a cervical tissue pathological section or a cervical TCT slide; the cell detection and segmentation module is used for extracting spatial position information and morphological characteristics of cells; the cell feature extraction module is used for extracting and fusing the spatial position, morphology, texture and biological marker features of the cells; the cell relation graph construction module is used for constructing a heterogeneous cell relation graph with cells as nodes and inter-cell relations as edges; the graph neural network analysis module is used for carrying out feature learning and modeling on the heterogeneous cell relation graph; the intelligent auxiliary diagnosis module is used for generating auxiliary diagnosis suggestions; and the data management and automatic control module is used for realizing automatic control and case data management of the whole process of the data. The intelligent and automatic level of cervical lesion cell analysis can be comprehensively improved, and the accuracy and efficiency of diagnosis are improved.

The embodiment of the invention relates to the technical field of bioinformatics, and provides a bulk data cell analysis method, device and equipment, and the method comprises the following steps: constructing an initial reference matrix according to a single cell data set and a cell type annotation template, each element in the initial reference matrix represents the gene expression quantity of each cell state under each characteristic gene; performing deconvolution on the bulk data to be analyzed according to the initial reference matrix to obtain a first deconvolution result; updating the initial reference matrix according to the first deconvolution result to obtain a first reference matrix; and according to the first reference matrix, performing deconvolution on the bulk data to be analyzed to obtain a second proportion and a second gene expression quantity of each cell type in the bulk data to be analyzed. According to the embodiment of the invention, the accuracy of cell analysis in bulk data can be improved.

A blood cell analyzer (100) and a blood cell analysis method (300). The blood cell analysis method (300) comprises: sucking a blood sample to be tested; preparing a first determination sample and obtaining first optical information; preparing a second determination sample and obtaining second optical information; generating a first leukocyte classification scatter diagram and a second leukocyte classification scatter diagram; determining a first counting result of neutrophils of the to-be-detected blood sample based on the first leukocyte classification scatter diagram and the second leukocyte classification scatter diagram; determining an actual distribution area of neutrophils from the first leukocyte classification scatter diagram; based on a reference distribution region of neutrophils of a reference leukocyte classification scatter diagram of the normal blood sample, determining a leaf-division nucleophore distribution region from the actual distribution region; determining a second counting result of the cells falling into the leaf-separating nucleogranulocyte distribution area; and based on the first counting result and the second counting result, determining a counting result of the rod-shaped nuclear granulocytes in the to-be-detected blood sample.

The invention relates to the field of cell analysis, and provides a multi-mode digital micro-fluidic system and method for cell sorting analysis, the system comprises an active digital micro-fluidic platform, the active digital micro-fluidic platform comprises a micro-fluidic chip, an upper computer control system and an FPGA hardware circuit, an upper computer generates a liquid drop path planning instruction, the FPGA hardware circuit converts the liquid drop path planning instruction into an electrode driving signal, and the electrode driving signal is transmitted to the micro-fluidic chip; the nanoliter-grade liquid drops are driven to change between the electrodes; the optical tweezers platform comprises a laser device, an optical microscope and a CMOS camera, laser beams are generated by the laser device to form a three-dimensional optical potential well, the laser beams are transmitted into the micro-fluidic chip through the optical microscope to realize non-contact control on cells, and the CMOS camera records images and transmits the images back to the upper computer; the objective table clamp is used for realizing cooperative control of the optical tweezers and the active digital microfluidics; and the image recognition system is used for recognizing morphological characteristics of cells in the liquid drops based on a convolutional neural network and screening target cells. According to the invention, integration of high-throughput nondestructive cell sorting and in-situ multi-modal analysis is realized.

The invention relates to the technical field of blood cell analysis, and particularly discloses a blood cell analysis system and method based on computer vision, which uses an image recognition and generation technology based on computer vision to carry out grid division and semantic feature extraction on blood image data. Performing global text generation and semantic coding on each blood image data grid block feature after semantic extraction so as to perform adjustment response aggregation on attention of different blood local areas in each blood image data grid block feature according to blood image text description semantic features; therefore, intelligent optimization of the blood image data is realized. In this way, the complex deep-layer mutual influence association relationship between cells can be captured, so that a more accurate diagnosis basis is provided. Meanwhile, the processing strategy can be dynamically adjusted according to the specific description content of the blood image so as to better adapt to diversified samples, and the method does not only depend on fixed preprocessing parameters.

The invention belongs to the technical field of single cell imaging and analysis, and provides an unmarked single cell two-dimensional light scattering imaging modal amplification method and system. The method comprises the following steps: acquiring a two-dimensional light scattering image of an unmarked single cell, and preprocessing the two-dimensional light scattering image; according to the proposed fusion condition adversarial loss LcGAN, L1 distance loss LL1 and a new imaging modal amplification model of perception loss # imgabs0 # based on a VGG network, generating a corresponding single-cell bright field image and a fluorescence image by using the processed light scattering image, and realizing amplification of a single-cell multi-modal image; multi-modal fusion and classification identification are carried out on the single cell two-dimensional light scattering image, the corresponding bright field image and the fluorescence image, so that the cancer single cell analysis accuracy is improved; the method can generate the corresponding single-cell bright field image and fluorescence image according to the unmarked single-cell two-dimensional light scattering image, is especially suitable for diagnosis and classification of cancer cells, and facilitates promotion of biomedical research and clinical application.

The invention discloses a method for determining a hydrogen and water relative permeability curve of an underground hydrogen storage and related equipment, and relates to the field of underground space engineering.The method comprises the steps that a microscopic computed tomography image of a rock sample in the hydrogen flooding process is obtained; performing characterization unit analysis on the pore structure of the microscopic computed tomography image to construct a first pore structured grid model, the first pore structured grid model being used for characterizing the pore structure of the rock sample, and the first pore structured grid model comprising a coordinate system of the pore structure; by taking a coordinate system of the first pore structured grid model as a reference, mapping the wettability distribution data to the first pore structured grid model through the corresponding coordinate position to obtain a second pore structured grid model, the wettability distribution data is calculated along a three-phase contact line of rock, hydrogen and water extracted from the microscopic computed tomography image; and performing water-driven hydrogen two-phase flow simulation operation on the second pore structured grid model to obtain a hydrogen and water relative permeability curve of the hydrogen storage bank.

The invention relates to the field of cell analysis, in particular to a cell chamber real-time feedback analysis system based on multi-parameter fusion weighting. Comprising a multi-parameter acquisition module for acquiring cell growth related parameter information in a cell chamber; an artificial intelligence algorithm model is built in the weight calculation module, and the weights of the multiple pieces of cell growth related parameter information are dynamically adjusted through machine learning; the parameter fusion module is used for fusing the multiple pieces of cell growth related parameter information according to the weight calculated by the weight calculation module; the real-time feedback module is used for generating feedback information in real time according to the fusion information; the visual display module is used for carrying out visual display on the feedback information; and the adaptive learning module is used for collecting experimental result information in the cell culture process, so that the weight calculation module optimizes the artificial intelligence algorithm model. According to the method, related parameter information of growth of each cell is dynamically adjusted according to the actual condition of cell culture, and fusion is carried out according to the weight, so that the requirements of modern cell research and application are met.

According to the liquid drop sorting instrument based on fluorescence detection, an inverted microscope serves as a whole instrument base and is located at the bottom, an XY precise moving platform is fixed to the microscope, a z-axis moving platform is fixed to a simple supporting frame beside the microscope, a fluorescence channel is installed on the z-axis moving platform, and a microfluidic liquid drop sorting module is fixed to the tip end of the fluorescence channel. A fluorescence detection module is arranged on the side wall of the fluorescence channel, the fluorescence detection module and the microfluidic liquid drop sorting module are both connected with the signal end of the FPGA signal processing module, the fluorescence detection module and the microfluidic liquid drop sorting module are controlled through the FPGA, the condition of generated liquid drops is observed through a microscope, the sorting process of the liquid drops is dynamically adjusted, and the quality of the liquid drops is improved. The closed-loop control of fluorescent liquid drop sorting is realized; according to the invention, high-precision and high-throughput liquid drop sorting can be realized, the liquid drop screening efficiency is greatly improved, the target liquid drop can be accurately identified and selected, and the method can be widely applied to the fields of high-throughput screening, single cell analysis, enzyme evolution, drug discovery and the like.

The invention relates to the technical field of cell analysis, and discloses a cell analysis method, a cell analysis device and a storage medium, and the method comprises the following steps: S1, multi-modal labeling: synchronously loading cell surface protein, a metabolism probe and a spatial positioning marker; s2, collaborative acquisition: obtaining space-time multi-dimensional data through flow type, mass spectrum and two-photon combination; s3, tensor modeling: constructing four-dimensional data and correcting spatial drift; s4, metabolic constraint decomposition: executing non-negative tensor decomposition to obtain a factor matrix; s5, dynamically adjusting parameters: optimizing excitation and sampling frequencies in real time according to factor changes; and S6, feature analysis: calculating a metabolism-phenotype coupling index. Through a multi-modal synchronous marking and four-dimensional dynamic tensor modeling method, cross-dimensional association of physical characteristics, molecular expression and metabolic activity is broken through, lossless fusion of single-cell multi-dimensional data is realized, and the spatial resolution is greatly improved compared with the prior art.

Disclosed herein are devices and methods for isolation and analysis of exosomal molecules from cell DNA samples. Such devices and methods facilitate isolation of ecDNA for downstream applications, such as cellular analysis and design of probes or therapeutic agents, in order to detect or target ecDNA for degradation or cells carrying ecDNA. Similarly, disclosed herein are compositions enriched for ecDNA and their use in the development of therapeutic or diagnostic agents.

A system for measuring an antibody concentration in a sample includes a polarizer holder holding a first detection polarizer and a second detection polarizer, the first detection polarizer restricting fluorescence emitted from within a container to pass in a first direction. The second detection polarizer restricts fluorescence emitted from within the container to pass in a second direction. The second direction is perpendicular to the first direction. The system includes a detector for measuring fluorescence emitted in a first direction and fluorescence emitted in a second direction. The system rotates the polarizer holder in an alternating direction about an axis of rotation such that the first detection polarizer and the second detection polarizer are alternately placed in an optical path of fluorescence emitted from within the container.

The application discloses a micro-fluidic chip and a micro-fluidic system, and relates to the technical field of single-cell analysis. The micro-fluidic chip comprises a first sample inlet module and a second control module. The first sample inlet module has a first detection position and a second detection position. The first sample inlet module is used for providing first particles and second particles. The first detection position is used for detecting the first particles. The second detection position is used for detecting the second particles. The first sample inlet module transports the detected first particles and second particles through a symmetrically arranged flow channel structure. In addition, the first detection position and the second detection position are symmetrically arranged, so as to shorten the time difference of the first particles and the second particles flowing to the pairing flow channel, and to improve the problem of hysteresis of any one of the first particles and the second particles in the pairing process.

The present invention discloses a method and device for analyzing cells in whole blood stock solution, belonging to the field of cell analysis. The whole blood stock solution is injected into an offset groove of a microfluidic chip; when the cells move, they encounter an obstacle column, and due to the flow resistance between the cells and the obstacle column, the cells migrate to a preset position in the offset groove; the target cells enter a focusing groove through a first filtering hole in a cell filter plate, and the plasma enters the focusing groove through a second filtering hole, and the plasma is located on both sides of the target cells; the remaining part continues to move along the offset groove and flows out from a first liquid outlet; the plasma pushes the target cells to move along the focusing groove to form a single-cell flow; the single-cell flow is optically detected; the detected target cells and plasma flow out from a second liquid outlet. Through the above steps, no additional reagents are used during the formation of the single-cell flow, and the blood can be transfused back into the body after analysis; a pure physical structure is adopted during the formation of the single-cell flow, and problems such as background noise caused by ultrasonic focusing will not occur.

The application discloses a multi-omics pathological analysis system and method for predicting the risk of colorectal liver metastasis, relates to the technical field of biomedical and cancer diagnosis, and comprises a data acquisition module, a data preprocessing module, a cell analysis module, a score construction module, a model construction and training module and a prediction and evaluation module; the application realizes comprehensive and accurate prediction of the risk of colorectal liver metastasis by integrating multi-omics data, including single-cell data sets, spatial transcriptome data, batch RNA-seq data, clinical data and whole slide image WSIs; the cross-dimension data fusion and analysis not only improve the accuracy and reliability of prediction, but also provide a powerful tool for in-depth exploration of the molecular mechanism of tumor occurrence, development and metastasis, especially in the cell analysis module, identification and analysis of the malignant cell subpopulation LMTMCs triggering liver metastasis can reveal cell subtypes and molecular characteristics closely related to liver metastasis, thereby providing a scientific basis for formulating a personalized treatment plan.

The invention provides a cell activity detection method based on fluorescence labeling, and relates to the technical field of cell analysis. The cell activity detection method based on fluorescence labeling comprises the following steps: S1, co-incubating a cell sample to be detected with a mixed solution of propidium iodide, a JC-1 mitochondrial membrane potential dye, a DCFH-DA active oxygen probe, a Caspase-3 / 7 activated substrate and a Hoechst nuclear dye, completing synchronous multiple labeling, and generating a multi-labeled cell sample, S2, detecting the cell activity based on the multi-labeled cell sample, five-channel full-automatic scanning is carried out through a high-content imaging system, Z-stack images are collected, and a multispectral image data set is generated. The five probes are mixed and incubated at one time through a synchronous multiple labeling process, so that the operation time is remarkably shortened, and the risk of cell injury caused by multiple times of centrifugation is reduced; z-stack full-automatic scanning is combined with three-dimensional image acquisition, the capturing capability of weak fluorescence signals is improved through multi-focal plane fusion, and information loss caused by cell thickness or position offset in traditional two-dimensional imaging is overcome.

The invention discloses a rapid programmable generation device and method for a cascade droplet array. The device comprises a hydrophobic fan-shaped substrate, a scraper blade, a radially arranged hydrophilic stack point array and a driving device. Driving the mother liquid drops to move along the surface of the hydrophobic substrate through the rotation of the scraper, and dividing the mother liquid drops into micro-droplets residing in a hydrophilic area by utilizing the surface tension difference between the hydrophilic stack point and the hydrophobic substrate; the liquid drop volume is dynamically controlled by adjusting the rotating speed of the scraper blade or the diameter gradient distribution of the stack points, and the gradient liquid drop array is generated through single operation. The device does not need an external pump valve or a precise sealing device, has the advantages of open type passive operation, low cost, high flux, high stability and the like, is suitable for scenes of drug screening, single cell analysis, instant detection and the like, and remarkably improves the flexibility of micro-droplet generation and the experiment efficiency.

This invention relates to the field of sputum smear cytology examination technology, and discloses an automated sputum cell analysis method and system based on deep learning. The method includes specimen collection and preprocessing, image preparation and acquisition, data preprocessing and dataset construction, improved YOLOv8 algorithm training, cell identification and statistics, large-scale model analysis, and report generation. This invention automates sputum cell analysis, significantly reducing specimen processing and interpretation time. The efficiency of each specimen analysis is several times higher than manual microscopic examination, reducing labor costs and efficiently handling the demand for large-scale specimen testing. The improved YOLOv8 algorithm, through the C2f-DCNv3 module, SPD-Conv, and color and texture attention mechanisms, accurately captures the differential characteristics of similar cells, significantly improving cell classification accuracy, reducing subjective and fatigue errors in manual interpretation, and resulting in more objective and reliable results.

This invention discloses a live-cell analysis method and system based on a large model and formal verification. The method includes context injection and intent reasoning, receiving natural language and analyzing it using a large language model to infer the required algorithm modules and output candidate configuration files. Formal logical verification involves reading the candidate configuration files and converting them into standardized logical constraint code, which is then verified by a solver. Conflict self-healing and iteration are implemented: if the solver verification fails, the conflict core is identified, and the connection method of the candidate configuration file or algorithm module is modified according to an arbitration strategy, while updating the logical constraint code until the solver verification passes. An analysis pipeline is instantiated and constructed: if the solver verification passes, an executable analysis pipeline is built based on the final determined candidate configuration files for live-cell analysis. Analysis code is generated using a large language model and logically verified using formal methods to analyze live-cell microscopic images.

In the field of cell-based assays, i.e. cellular assays, there is a need for improved cell culture retaining inserts and cell culture retaining insert assemblies. A cell culture retaining insert (10; 70; 90; 100), for retaining a three-dimensional biological sample in a cell culture vessel (102; 132), comprises an insert body (12; 104) that has a positioning formation (16) to in-use locate the cell culture retaining insert (10; 70; 90; 100) relative to a cell culture vessel (102; 132). The insert body (12; 104) also has a retention element receiving formation (20). The cell culture retaining insert (10; 70; 90; 100) additionally includes a retention element (30; 60; 72; 92; 114) which is movably coupled with the receiving formation (20) of the insert body (12;104), whereby a distal retention end (32) of the retention element (30; 60; 72; 92; 114) is freely movable away from and towards the insert body (12; 104), and whereby in-use the weight of the retention element (30; 60; 72; 92; 114) less a buoyant force equal to the weight of any fluid the retention element (30; 60; 72; 92; 114) displaces gives rise to the retention end (32) exerting a retention force on one or more said biological samples.

This invention belongs to the field of microbial electrochemistry and single-cell analysis technology, and specifically relates to a femtocoulomb-level single-bacterial surface charge imaging method and its application. This technology is based on electrochemiluminescence microscopy, utilizing the spontaneous enrichment of cationic ECL luminescent molecules by the electrostatic field of the negative charge on the bacterial surface, thereby non-destructively enhancing its ECL signal. By establishing a quantitative relationship between the ECL intensity enhancement ratio and the number of adsorbed molecules, and combining this with COMSOL simulation to determine the thickness of the luminescent layer, it achieves for the first time high-throughput, high spatiotemporal resolution dynamic quantitative measurement of femtocoulomb-level charges on the surface of a single living bacterium. The invented technology has high sensitivity, high resolution, and good biocompatibility, and can be used to reveal bacterial metabolic-charge regulation mechanisms, analyze synergistic electron transport pathways, and screen highly electroactive bacterial subpopulations in situ, providing a key tool for microbial electrochemical research.

The invention relates to a cell morphology feature extraction and prediction analysis method, in particular to a cell morphology feature extraction and prediction analysis method for a high-throughput cervical TCT image. The method aims at solving the problems that in the cell analysis technology of the cervical TCT image, complex morphological characteristics of cells are not fully considered, the characteristic extraction precision is low, and the manual dependence degree is high. The method comprises the following steps: selecting cellular morphology characteristics of cervical TCT image auxiliary diagnosis; obtaining a segmentation result of the cell nucleus and the cytoplasm based on the segmentation network; extracting, measuring and calculating cell morphological characteristics based on a cervical TCT image segmentation result; and analyzing and visualizing the measurement and calculation results of the morphological characteristics of the cells. According to the method, medical priori knowledge and data-driven research methods are fused, and more interpretable quantitative data are provided for subsequent cervical cell research through conjoint analysis of shape features and texture features. The invention belongs to the technical field of medical image analysis and computer vision.

The present invention provides a cell impedance measurement device capable of measuring and collecting electrical data on cells and performing real-time cell analysis in a non-destructive manner, and a cell analysis system comprising same.

This application is applicable to the field of cell analysis technology, and particularly relates to a method and device for cell mechanical phenotype analysis based on deep learning. The method includes: acquiring temporal images, mechanical response data, and culture environment parameters of target cells; based on the mechanical response data and culture environment parameters, performing regional segmentation on the temporal images to determine the mechanically sensitive regions on the surface of the target cells and the local deformation characteristics of subcellular structures, thereby achieving refined and targeted feature recognition; generating a mechanical phenotype descriptor of the target cells according to the mechanically sensitive regions, local deformation characteristics, and culture environment parameters to realize multi-modal data fusion; using a mechanical phenotype analysis model pre-trained with sample mechanical phenotype descriptors of sample cells to process the mechanical phenotype descriptor, and obtaining a heterogeneity prediction result of the target cells, automatically identifying the heterogeneity of cell mechanical properties under complex culture environments, and significantly improving the dynamics and generalization ability of heterogeneity classification.

The invention relates to the technical field of cell analysis equipment, and provides a dynamic gradient mixing and conveying device of a chromatographic column and chromatographic column analysis equipment. The dynamic gradient mixing and conveying device of the chromatographic column comprises a sample injection bottle, a B-phase input unit, a mixed phase output unit and a liquid flow velocity control unit. An A phase is contained in the sample injection bottle, and the B phase input unit is connected with the sample injection bottle so as to input a B phase into the sample injection bottle to be mixed with the A phase to form a mixed phase. And the mixed phase output unit is connected with the sample injection bottle and the chromatographic column so as to convey the mixed phase in the sample injection bottle into the chromatographic column. The liquid flow velocity control unit is used for adjusting the flow velocity of the mixed phase conveyed into the chromatographic column. In the ultra-narrow inner diameter chromatographic analysis process, the flow velocity of a mixed phase entering the ultra-narrow inner diameter chromatographic column can be controlled through the liquid flow velocity control unit, so that the flow velocity is more stable, the proportion of the mobile phase can be changed in real time, the mixed phase entering the ultra-narrow inner diameter chromatographic column is ensured to be uniform, and the stability and reproducibility of an analysis result are further improved.

The invention is applicable to the field of cell analysis equipment, and discloses an optical detection device and a cell analyser, the optical detection device comprises a flow chamber, a light source and a forward scattering detection assembly, the forward scattering detection assembly is used for collecting forward scattering signals generated when the light source irradiates to-be-detected cells, and the forward scattering signals are sent to the flow chamber; the forward scattering detection assembly comprises a first focusing lens, a diaphragm assembly and a first detector, and the diaphragm assembly is arranged between the first focusing lens and the first detector; the first focusing lens is arranged between the flowing chamber and the diaphragm assembly, so that forward scattering light generated when the light source irradiates to-be-detected cells is converged and then penetrates through the diaphragm assembly to enter the first detector, the first focusing lens is a spherical lens, and the number of the first focusing lens is one. The single spherical lens is adopted to collect forward scattering signals and gather the forward scattering signals to the first detector, so that the size and the cost of the optical detection device are effectively reduced, and the miniaturization design of the cell analyzer is facilitated.

The application discloses a single-cell online prolonging lysis mass spectrometry flow analysis method. A cell suspension sample is injected into a separation capillary, a front section of the capillary is spirally wound to realize single dispersion of the cells, and a part of a rear section of the capillary is located in an ultrasonic device to realize online ultrasonic lysis, localized single-cell lysis contents are obtained, the content solution gradually enters a non-contact electrospray needle to realize ionization, and finally enters a mass spectrometry detector for detection. Meanwhile, the analysis coverage of weakly polar compounds can be improved by combining a plasma generation device. The method can realize single-cell analysis with high throughput and high metabolite coverage, and by means of a prolonged single-cell detection window, the collection of metabolite primary mass-to-charge ratio information and the identification of secondary structures under high resolution can be realized, including qualitative and quantitative analysis of positional isomers, so that the method has wide application prospects in the fields of cell identification, cell typing, differential substance research, tumor diagnosis and biomarker mining.

The present invention relates to a cell analysis system based on virtual fluorescence generation technology, and a method therefor. According to the present invention, the system comprises: an input unit that receives a quantitative phase image of a sample including at least one cell through digital holography microscopy; and a cell identification unit that identifies at least one living cell by applying, to the received quantitative phase image, a cell segmentation mask generation model and a nuclei segmentation mask generation model constructed previously, and that masks a position of the nucleus of the living cell to derive an identification result. The system may further comprise a post-processing unit that derives a final result by applying a preset post-processing method to the derived identification result.

An automated cell analysis device, an automated cell analysis method, and an automated cell analysis system. The automated cell analysis device comprises: a workbench; functional modules arranged on the workbench, wherein the functional modules are used for cell culture, analysis and / or detection; and a rail-mounted transfer robot arranged on the workbench, wherein the rail-mounted transfer robot comprises a rail and a robot, and the robot is arranged on the rail and can move along the rail to transfer a material between the functional modules.