317 results about "Cell analysis" patented technology

This document generally describes methods and systems for generating digital reconstructions of tissue from humans or other species. The method can, for example, include receiving, at a computing system, image data of a tissue sample, where one or more sections of the tissue sample are stained with hematoxylin and eosin (H&E), registering the image data to generate registered image data, identifying tissue subtypes based on application of a machine learning model to the registered image data, annotating the identified tissue subtypes to generate annotated image data, and determining a digital volume of the tissue sample in three dimensional (3D) space based on the annotated image data. The disclosed technology can provide for single-cell analysis and other analysis of tissue samples, such as early detection of cancer in human tissue samples.

The invention discloses a multi-omics pathological analysis system and method for colorectal cancer liver metastasis risk prediction, and relates to the technical field of biomedicine and cancer diagnosis, the system 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; according to the method, multiple omics data including the single cell data set, the space transcriptome data, the batch RNA-seq data, the clinical data and the full slice image WSIs are integrated, comprehensive and accurate prediction of the colorectal cancer liver metastasis risk is achieved, cross-dimensional data fusion and analysis improve the prediction accuracy and reliability, and the risk of the colorectal cancer liver metastasis is reduced. The invention also provides a powerful tool for deeply exploring the molecular mechanism of tumorigenesis, development and metastasis, and particularly in a cell analysis module, cell subtypes and molecular characteristics closely related to liver metastasis can be disclosed by identifying and analyzing malignant cell subgroups LMTMCs triggering liver metastasis, so that the liver metastasis can be discovered. And a scientific basis is provided for formulating a personalized treatment scheme.

The invention is suitable for the technical field of cell analysis, and particularly relates to a cell mechanical phenotype analysis method and device based on deep learning, and the method comprises the following steps: obtaining a time sequence image, mechanical response data and culture environment parameters of a target cell; based on the mechanical response data and the culture environment parameters, performing region segmentation on the time sequence image, determining a mechanical sensitive region on the surface of the target cell and local deformation features of a subcellular structure, and realizing refined and targeted feature recognition; generating a mechanical phenotype descriptor of the target cell according to the mechanical sensitive area, the local deformation characteristics and the culture environment parameters to realize multi-modal data fusion; a mechanical phenotype analysis model obtained by training a sample mechanical phenotype descriptor of a sample cell in advance is used for processing the mechanical phenotype descriptor to obtain a heterogeneity prediction result of a target cell, the heterogeneity of the mechanical characteristics of the cell in a complex culture environment is automatically identified, and the dynamic nature and generalization ability of heterogeneity classification are remarkable.

The invention discloses an operation method of a cell operation platform combining a piezoelectric actuator and a six-axis mechanical arm, which integrates the large-stroke positioning capability of the mechanical arm and the nano-scale fine operation capability of the piezoelectric actuator, and combines the large-stroke positioning capability of the mechanical arm and the nano-scale fine operation capability of the piezoelectric actuator on the basis of macroscopic mechanical arm movement. The piezoelectric driving unit can accurately approach and control target cells in various postures, so that cell-level fine operation tasks such as cell surface treatment, single cell grabbing and cell injection are completed at high precision, and reliable technical support is provided for single cell analysis and operation.

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 invention discloses a cell image segmentation method, and relates to the technical field of cell analysis, and the method comprises the following steps: S1, image acquisition; s2, processing optical artifacts generated by imaging of the phase contrast microscope; s3, processing interference of impurities and debris in the cell image; in the method, a phase contrast microscope and an optical coherence tomography microscope are combined to obtain a cell image, three-dimensional structure information of an OCT image is utilized to assist in correcting artifacts generated by imaging of the phase contrast microscope, and the artifacts are processed based on phase recovery and filtering and texture feature matching and repairing. According to the method, the interference of impurities and debris is removed by applying a diffusion and repair method based on a partial differential equation and a machine learning classification and reconstruction method, so that the cell image quality is effectively improved, the interference of artifacts and impurities on subsequent analysis is reduced, and a reliable image data basis is provided for cell image segmentation and cell morphology analysis.

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.

The invention provides a cell analyzer and a cell analysis method.The cell analyzer comprises a chip layer and a runner layer, a liquid inlet, an impedance measuring unit and a liquid driving unit are arranged in the chip layer, the liquid inlet is communicated with a first surface and a second surface of the chip layer, the impedance measuring unit comprises an electrode plate exposed on the first surface of the chip layer, and the liquid driving unit is arranged on the first surface of the chip layer. The liquid driving unit comprises a driving piece exposed on the first surface of the chip layer, the runner layer is combined with the first surface of the chip layer and comprises a runner and a nozzle, the runner is formed in the face, facing the chip layer, of the runner layer, the liquid inlet, the runner and the nozzle are sequentially communicated, and an outlet of the nozzle is formed in the face, away from the chip layer, of the runner layer. At least one part of the electrode plate is located in the flow channel, and at least one part of the driving plate faces the outlet of the nozzle. The cell analyzer provided by the invention can finish accurate quantitative transportation of liquid to be detected and accurate counting of specific cells, realizes accurate measurement of the number and concentration of cells on a chip, and has the characteristics of miniaturization, automation and low cost.

The invention discloses an experimental method based on depletion-type nk cells as a lung cancer treatment target, and relates to the technical field of bioinformatics. Comprising the following steps: S1, data processing; s2, identification of cell subset characteristic genes and cell type annotation; s3, re-clustering the NK cells; s4, NK cell function annotation; s5, differential gene expression analysis; s6, analyzing the difference of activation and inhibition of receptor expression by the RGS1 + NK cells through single cells; s7, performing quasi-time sequence analysis; s8, carrying out cell culture and siRNA transfection; s9, detecting the expression condition of an NK92mi cell activated receptor after the RGS1 is knocked down; s10, carrying out a lactic dehydrogenase release experiment; s11, measuring the IFN-gamma content of the cell supernatant; and S12, virtual screening of drugs. Through analysis and experimental verification, the key function of RGS1 in NK cell depletion in lung cancer is disclosed, and a new and potential molecular target is provided for lung cancer treatment.

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 provides a tumor microenvironment cell spatial distribution characteristic quantitative analysis method and system, and the method comprises the steps: obtaining a pathological section image, and obtaining cell segmentation and classification information in the pathological section image through a pre-trained cell recognition model; obtaining cell space distribution information based on the cell segmentation and classification information; constructing a cell topology network according to the cell space distribution information, and obtaining a first cell space distribution characteristic; constructing a spatial weight matrix, and obtaining a second cell spatial distribution feature; and judging a tumor state by combining the first cell spatial distribution characteristic and the second cell spatial distribution characteristic so as to realize typing of the target object. The method has good cell segmentation and classification precision, fully pays attention to the adjacency relation, the action intensity and the space structure between cells, integrates multi-dimensional space distribution characteristic quantitative analysis, realizes a good tumor microenvironment cell analysis result, is used for accurately realizing the typing of a target object, and has good universality.

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 relates to a fluorescence in-situ hybridization image result analysis method and system based on deep learning, and a medium. The method comprises the following steps: constructing an SEAM-Unet + + image segmentation model; a YOLO-SEM small target detection algorithm model is constructed; obtaining a cell precise segmentation map of a channel B; obtaining a red fluorescence probe distribution map; obtaining a green fluorescent probe distribution map; and combining the obtained accurate cell segmentation map, the red fluorescence distribution map and the green fluorescence distribution map into one map to obtain a relational graph of cell and probe distribution. According to the method, complex image scenes can be effectively processed, the analysis of various medical images is supported, the medical diagnosis efficiency is improved, personal errors are reduced, and the cell analysis and pathological detection capabilities in scientific research and clinical application are enhanced.

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 discloses a specific cell population analysis and screening method based on RCA-FISH and application of the specific cell population analysis and screening method. A target sequence region which is high in hybridization efficiency, high in specificity and free of an RNA advanced structure is screened out from an RNA sequence of a cell or tissue to be detected to design a primary hybridization probe, and the primary hybridization probe is composed of a lock-type probe and an RCA initiation probe; the method comprises the following steps: pre-treating a primary probe, hybridizing, carrying out a probe cyclization connection reaction, amplifying a signal by rolling circle amplification, and labeling cells by a fluorescent probe, so as to be used for flow cytometry analysis / sorting. The invention has the characteristics of simple probe preparation, high detection efficiency, strong specificity, strong signal amplification and the like.

The present invention provides an information processing method, device, equipment and storage medium based on cell segmentation, which relates to the field of biological information technology. The method includes: obtaining a corrected scanned image; the scanned image is obtained based on a tissue slice attached to a biochip; performing cell segmentation on the scanned image to obtain a cell segmentation result; based on the inherent position of each barcode microsphere in the biochip, extracting the position information of the barcode microsphere corresponding to each cell in the cell segmentation result; based on the position information of the barcode microsphere corresponding to each cell in the cell segmentation result, determining the first position information of each cell and the statistical information of the corresponding barcode microsphere. The present invention can improve the efficiency and accuracy of cell analysis.

Provided herein are methods, systems, and compositions for cell storage and cell analysis. In some embodiments, compositions such as buffers are provided that enhance the stability of samples comprising one or more cells and / or constituents of one or more cells under storage conditions. The methods and compositions are compatible with samples comprising small numbers of cells and single cells and reduce or prevent sample loss, cell loss from samples, and adverse effect on screening results caused by storage, temperature fluctuations, and sample shipment. Methods and compositions may enable sample storage for genomics and multiomics analysis and primary template amplification.

To determine the type of cells which an existing scattergram cannot determine.SOLUTION: The present invention relates to a method for analyzing cells which analyzes a cell in a living body sample by a deep learning algorithm of a neural network structure, the method including the steps of flowing the cell in a flow passage, acquiring a signal intensity related to individual cells passing through the flow passage, inputting the numerical value data corresponding to the signal intensity related to the acquired individual cells into the deep learning algorithm, and determining the type of cells of which signal intensity has been acquired on a cell-by-cell basis on the basis of the result output from the deep learning algorithm.SELECTED DRAWING: Figure 1

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.

Disclosed is a single cell analysis device for analyzing a tumor using artificial intelligence, the device including, a memory, and a processor, and the processor is configured to acquire single cell data on a tumor of a particular patient through the communication part; pre-process the single cell data on the tumor through a data pre-processing module; classify a type of the single cell based on the data pre-processed by a cell type defining module; predict a phenotype derivable from RNA expression of the tumor of the particular patient through a single-cell state predicting module materialize heterogeneity of the tumor of the particular patient through the tumor analyzing module; and report and provide an analysis result of the tumor of the particular patient through an analysis result generating module.

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.

Disclosed herein are methods for preparing a biological sample for single-cell analysis which will simultaneously provide single-cell RNA, single-cell extracellular protein, single-cell cytoplasmic protein, and single-cell transcription factor readouts while preserving cellular heterogeneity. Some methods disclosed herein allow for the detection of proteins including the detection of the level of proteins and / or the detection of post translational modifications of proteins.