39 results about "Tissue space" patented technology

The invention discloses an intestinal cancer immune response graph neural network prediction method, a medium and equipment, and the method comprises the steps: collecting pathological image information, immunodetection information and basic clinical information, extracting a tissue space distribution characteristic spectrum through a deep convolutional network, and constructing a graph neural network model in combination with an immunomarker expression characteristic matrix; spatial interaction characteristics of a tumor microenvironment are modeled by adopting a graph attention mechanism, finally a treatment response probability, an optimal treatment opportunity and an adverse reaction risk are predicted through a multi-task learning framework, and a clinical decision report containing a prediction response curve, a risk early warning threshold and a treatment time window suggestion is output. According to the method, through multi-modal data fusion and spatial interaction modeling, accurate prediction of intestinal cancer immunotherapy response is realized, and a more comprehensive reference basis is provided for clinical decision making.

The invention provides a spatial domain identification method based on data interpolation and cell type deconvolution, and belongs to the technical field of bioinformatics. In order to solve the problems that gap information between adjacent points cannot be utilized in low-resolution spatial transcriptome data and prior information of cell types in a tissue space structure level cannot be fully integrated in a traditional method, the method comprises the following steps: acquiring a spatial transcriptome data set and a single-cell RNA sequencing data set, and performing data preprocessing on the acquired data sets; and carrying out data interpolation on the preprocessed spatial transcriptome data, and carrying out cell type deconvolution in combination with single-cell RNA sequencing data. And constructing a deep learning model based on the graph convolutional network. And training a deep learning model according to gene expression information, spatial position information and cell type information of the spatial transcriptome data after cell type deconvolution by using a self-supervised contrast learning strategy. And performing spatial domain identification on the to-be-detected data based on the trained model.

The invention discloses the technical field of pathology and space transcriptomics, and particularly relates to a gene expression prediction method and system based on multi-modal comparative learning and a guidance mechanism. Cutting the histological slice image into image blocks according to space coordinates; according to the method, a local convolution branch and a global Transform branch are combined to extract image features, the image features are mapped to a shared potential space through projection, soft contrast, hard contrast and global consistency constraints are introduced into the space, and cross-modal alignment of an image modal and a gene expression modal is realized; an expression prediction head is introduced in the training stage, representation learning is directly guided by a regression signal, and the relation between feature learning and gene expression prediction is broken through; in the inference stage, k-nearest neighbor retrieval and a multi-distance weighted aggregation strategy are combined to infer a gene expression profile of an unknown position. According to the method, the accuracy and robustness of space gene expression prediction can be effectively improved, the tissue space heterogeneity structure is kept, and the method has high clinical application and scientific research and popularization value.

A tissue traction device configured to apply traction to a target tissue within a patient's body by attaching an end of the tissue traction device to anchoring tissue spaced apart from the target tissue. The tissue traction device includes at least one weakened region facilitating severing of the tissue traction device therealong, as compared with surrounding non-weakened regions. The at least one weakened region may be visually identifiable to facilitate severing of the tissue traction device therealong within the patient. The tissue traction device may be formed of distinctly-formed sections, such as, without limitation, enclosed shapes with an aperture therethrough, such as loops.

The invention provides a space transcriptomics organization space domain intelligent identification method based on a Graph Transform. The method comprises the following steps: reading a space transcriptomics data file; extracting features in the histological image data by adopting a pre-trained deep learning model; setting a spatial distance threshold value; acquiring spatial position data of each sampling point according to a set spatial distance threshold value, and constructing an adjacent matrix; combining the gene expression data, the adjacent matrix and the histological image features to obtain a normalized feature matrix; performing dimensionality reduction on the normalized feature matrix by adopting PCA; inputting the feature matrix subjected to dimension reduction processing into a feature learning model to obtain a comprehensive feature representation; clustering the obtained feature representations by adopting a Leiden algorithm to obtain a spatial domain classification result; according to the invention, deep fusion of multi-modal information is realized through a Graph Transform architecture, spatial distribution rules and functional characteristics of cells can be captured at the same time, and a systematic solution is provided for analyzing a complex structure of a tissue microenvironment.

The invention discloses a method for constructing a human marrow paraffin tissue space transcriptome sequencing map, which comprises the following steps of: obtaining high-quality space transcriptome data from a marrow paraffin section of an AML (acute myeloid leukemia) patient, and then integrating the space transcriptome with scRNA-seq and histopathology. Relatively complete transcriptome information is still preserved after the bone marrow specimen is subjected to better decalcification treatment, and meanwhile, higher-quality slicing treatment can be performed on the embedded paraffin tissue, so that very complete bone marrow space transcriptome sequencing data is obtained. Then, the cell type of each detection site in the bone marrow space transcriptome map can be accurately defined by integrating bone marrow single cell sequencing data and pathological morphology annotations, and subsequent analysis is carried out.

Provided herein are compositions, kits and methods for the collection, integration and analysis of spatially-based information from tissues at the cellular or subcellular level. Layers of information include, without limitation, gene expression, morphology, nucleic acid and protein. In some workflows, light-based technology is incorporated for selective spatial barcoding of regions. In a further step, such barcoded tags are analyzed by high-throughput next-generation sequencing, if necessary.

A method of placing an inflatable spacer inside a living body includes inserting a portion of a dilator assembly into a tissue space inside a living body. The dilator assembly includes a dilator sheath having an open distal end, a dilator core, and a sensor attached to the dilator core. The sensor is responsive to a condition inside the body and transmits sensor feedback to a receiver during positioning of the dilator assembly inside the living body. The inserting includes inserting the dilator assembly into the tissue space to position, based on the sensor feedback, the tapered distal end of the dilator core at a target location inside the living body. The method also includes removing the dilator core from the dilator sheath, inserting a spacer into the living body through the open distal end of the dilator sheath, and removing the dilator sheath from the living body.

A method and system for extracting tissue optical parameters based on a long short-term memory network belongs to the technical field of the intersection of machine learning and biomedicine, and solves the problem of inaccurate extraction of tissue optical parameters caused by only utilizing part of the diffuse reflection light information and ignoring the correlation of spectral data sequence characteristics when extracting tissue optical parameters. The present invention introduces a long short-term memory network into a tissue spatial resolution diffuse reflection spectrum generation model, fully explores the correlation in spectral data points, and increases the model prediction ability while reducing model parameters. When training the tissue spatial resolution diffuse reflection spectrum generation model, the optical fiber probe structure is not considered, and all spatial resolution diffuse reflection spectra emitted from the tissue surface are included in a pre-data set, the data dimension is larger, and the model accuracy is higher after training, which effectively improves the accuracy of the tissue optical parameter and diffuse reflection spectrum mapping model, thereby improving the efficiency and accuracy of tissue optical parameter extraction.

A tissue traction device having more than one grasping segment by which the tissue traction device may be engaged with tissue directly, or indirectly, such as with the use of a tissue-engagement member. The tissue traction device may have a peripheral section defining grasping segments, as well as interior grasping segments defined within the perimeter of the peripheral section. Traction may be applied to a target tissue by engaging a target-tissue-engaging segment with target tissue, and engaging a traction-tissue-engaging segment with traction tissue spaced apart from the target tissue. Engagement of additional target-tissue-engaging segments with traction tissue (optionally at different locations) allows the force vector of the traction to be adjusted. A delivery system may include tissue traction device holder to maintain a desired configuration of the tissue traction device during delivery thereof to a target tissue area.

The application discloses a multi-modal graph fusion driven spatial transcriptome region identification method, and belongs to the field of biomedical informatics and computational biology. The method comprises the following steps: step 1, data acquisition; step 2, data processing; step 3, multi-modal adjacency matrix generation; step 4, differentiable multi-modal graph structure fusion; step 5, variational graph autoencoder training; and step 6, clustering. By constructing a spatial domain identification framework based on multi-modal graph fusion and variational autoencoder, the application significantly improves the accuracy, continuity and biological interpretability of spatial transcriptome clustering analysis, effectively solves the deficiencies of existing methods in multi-modal fusion, graph structure optimization and feature discriminability, and provides a more reliable computing tool for in-depth revealing of tissue spatial heterogeneity in the fields of brain science, tumor microenvironment analysis and other frontier biomedical researches.

This invention proposes a method for spatial domain identification based on data interpolation and cell type deconvolution, belonging to the field of bioinformatics. This invention addresses the problems of traditional methods failing to utilize spatial gap information between adjacent points and insufficient integration of prior information about cell types at the tissue spatial structure level in low-resolution spatial transcriptome data. The method includes: acquiring spatial transcriptome datasets and single-cell RNA sequencing datasets, and preprocessing the acquired datasets; interpolating the preprocessed spatial transcriptome data and performing cell type deconvolution in conjunction with the single-cell RNA sequencing data; constructing a deep learning model based on a graph convolutional network; training the deep learning model using a self-supervised contrastive learning strategy based on gene expression information, spatial location information, and cell type information from the deconvolved spatial transcriptome data; and performing spatial domain identification on the test data based on the trained model.

A device, system, and method for engaging tissue and / or applying traction to tissue. A first component of a tissue-engaging device engages a first section of tissue while a second component of the tissue-engaging device engages a second section of tissue spaced apart from the first section of tissue. The first component may be configured to grasp target tissue at the first section, whereas the second component may be configured to simply engage, but not grasp, anchor tissue at the second section. Thus, the second component may grasp anchor tissue with the assistance of the second component, but may be readily removed from the anchor tissue without damaging the anchor tissue while the target tissue remains grasped by the second component.

Provided herein is a cell macroencapsulation device composed of hydrogel in a 3D conformation that optimizes encapsulated cell viability and function when transplanted into a vascularized tissue space. The hydrogel macroencapsulation device is intended to reduce or eliminate immune response to the cell graft, while allowing exchange of encapsulated cell-secreted products, such as insulin. Also described herein is an injection-mold and fabrication process to generate the hydrogel macroencapsulation devices for use in the clinic.

The invention relates to the technical field of pathological image processing and medical artificial intelligence auxiliary diagnosis, and discloses a colorectal cancer prognosis analysis system and method fusing tissue and block characteristics, and the system comprises an image block cutting and standardization module which generates a standardized image block set under different multiplying power; the ResNet50 classifier module is used for carrying out organization classification on the image blocks; the tissue type specific feature extraction module is used for extracting multi-scale survival correlation features; the tissue spatial feature extraction and quantitative analysis module is used for reconstructing a multi-tissue type distribution diagram and calculating quantitative spatial features such as the number of lymphocytes and the intraperitoneal ratio after morphological repair; and the multi-modal feature fusion and survival prediction module is used for fusing the pathological deep features, the spatial segmentation features and the clinical data, and outputting an individualized prognosis risk score by using a survival model. According to the method, a macrostructure and microcosmic details are effectively integrated, and the accuracy and interpretability of colorectal cancer survival risk layering are improved.

The invention discloses a tissue slice target protein multiple analysis method based on reversible DNA self-assembly, comprising step one, design of a DNA self-assembly system, step two, synthesis of an antibody Ab-T, step three, pretreatment and imaging of the target protein of the tissue slice, and step four, image processing of imaging analysis; the invention combines DNA self-assembly system design, click chemistry reaction, immune recognition, reversible DNA self-assembly and tissue fluorescence imaging technology, outputs multiple target proteins on the tissue slice as multiple fluorescence signals, realizes multiple analysis of the target proteins on the tissue slice, reduces time cost and demand for tissue samples without reducing imaging accuracy; and has important significance for basic scientific research, clinical research and application such as spatial localization relationship analysis of multiple target proteins of tissue samples, especially precious tissue samples, cell typing analysis, tissue spatial distribution analysis of cells, etc.

A surgical stapling device includes a cartridge assembly for accommodating tissue of different thicknesses. The cartridge assembly includes a channel member and a staple cartridge received in the channel member for movement between positions to vary a size of a tissue gap defined between the staple cartridge and an anvil of the stapling device. The cartridge assembly also includes features formed on a pusher or cartridge body of the staple cartridge of the cartridge assembly to improve staple formation when suturing thin tissue.

The invention provides a method and a system for dynamically optimizing an energy field of a temperature flux instrument based on multipath acoustic characteristics, and relates to the technical field of temperature flux instruments, and the method comprises the following steps: transmitting a detection sound wave, and receiving acoustic echo data which is from human tissues and contains a multipath propagation effect; extracting a multipath time delay characteristic and a frequency dispersion characteristic which are related to an organization space structure, and reversely reconstructing a three-dimensional acoustic energy absorption distribution diagram of a target area below the physiotherapy end; calling a preset expected energy distribution template, and comparing the three-dimensional acoustic energy absorption distribution map with the preset expected energy distribution template in real time to generate a difference map; and according to the difference chart, generating and executing a spatial modulation instruction for an output energy field of the temperature ventilation instrument in real time so as to dynamically optimize deposition distribution of energy into the target area. According to the scheme, energy distribution uncertainty caused by tissue space heterogeneity and dynamics is overcome, and it is ensured that treatment energy acts on a target area accurately, safely and efficiently.

The invention discloses a spatial transcriptome region identification method driven by multi-modal graph fusion, and belongs to the field of biomedical informatics and computational biology. The method comprises the following steps: step 1, data acquisition; 2, data processing; step 3, generating a multi-modal adjacent matrix; 4, carrying out the structure fusion of the differentiable multi-modal graph; step 5, variational graph self-encoder training is carried out; and step 6, clustering. According to the method, the spatial domain recognition framework based on multi-modal graph fusion and the variational autoencoder is constructed, so that the accuracy, continuity and biological interpretability of spatial transcriptome clustering analysis are remarkably improved, and the defects of an existing method in the aspects of multi-modal fusion, graph structure optimization and feature discrimination are effectively overcome; and a more reliable calculation tool is provided for deeply revealing tissue spatial heterogeneity in the field of advanced biomedical research such as brain science, tumor microenvironment analysis and the like.

A tissue traction device configured to apply traction to a target tissue within a patient's body by attaching an end of the tissue traction device to anchoring tissue spaced apart from the target tissue. The tissue traction device includes at least one weakened region facilitating severing of the tissue traction device therealong, as compared with surrounding non-weakened regions. The at least one weakened region may be visually identifiable to facilitate severing of the tissue traction device therealong within the patient. The tissue traction device may be formed of distinctly-formed sections, such as, without limitation, enclosed shapes with an aperture therethrough, such as loops.

The invention relates to a WSI image tissue classification method based on a domain adaptive learning technology. Compared with the prior art, the defect that the WSI image tissue classification effect is poor due to pathological sample process differences is overcome. The method comprises the following steps: acquiring a WSI image and preprocessing the WSI image; constructing a WSI image organization classification model; training a WSI image organization classification model; carrying out benefit reduction correction on the WSI image tissue classification model; obtaining a WSI image to be classified; and obtaining a WSI image organization classification result. The tissue microstructure and texture information are extracted by using the convolutional neural network, and different tissue space structure combinations and microenvironment changes are learned from a multi-resolution angle by using a pyramid structure, so that accurate tissue classification and recognition are performed on the WSI image, the tissue pathology image analysis efficiency is improved, and meanwhile, the tissue pathology analysis accuracy is improved. On the basis of a domain adaptive method, reduction and benefit correction is carried out on network performance degradation caused by cross-data-set application, and accurate classification of WSI image organizations is achieved.

The application discloses an intestinal cancer immune response graph neural network prediction system, medium and equipment, through collecting pathological image information, immune detection information and basic clinical information, using a deep convolution network to extract a tissue spatial distribution feature map, combining an immune marker expression characteristic matrix to construct a graph neural network model, adopting a graph attention mechanism to model spatial interaction characteristics of a tumor microenvironment, finally predicting a treatment response probability, an optimal treatment time and an adverse reaction risk through a multi-task learning framework, and outputting a clinical decision report containing a predicted response curve, a risk early warning threshold and a treatment time window suggestion. Through multi-modal data fusion and spatial interaction modeling, the application realizes accurate prediction of the intestinal cancer immunotherapy response, and provides a more comprehensive reference basis for clinical decision-making.

The invention relates to the field of biological information, in particular to a tissue space structure recognition method and device and application. The method comprises the following steps: determining a space in-situ image of a given tissue cell population according to space coordinate information of the given tissue cell population; generating a space structure of the given tissue based on the space in-situ image of the cell population of the given tissue; wherein the space coordinate information of the given tissue cell population is determined by the following steps: (a) based on tissue space transcriptome data, acquiring the tissue cell group information; and (b) annotating the tissue cell group information by using a given tissue characteristic gene set so as to determine space coordinate information of the given tissue cell group. Through the method, accurate self-service identification of the organization space structure without depending on professional expert knowledge can be realized.

The invention relates to the technical field of biological evaluation of medical instruments, in particular to a microfiltration membrane dynamic extraction-based cytotoxicity detection method which comprises the following steps: preparing a particle-containing gel sample to be detected into an extraction system; constructing a non-contact dynamic co-culture model containing a microfiltration membrane and cells; adding the leach liquor into the upper chamber, and carrying out dynamic leaching; cytotoxicity detection is carried out in the lower chamber. Particles are accurately intercepted through the micro-filtration membrane, detection errors caused by particle interference are avoided, the detection accuracy is improved, the authenticity of a detection result is ensured, 'false positive 'caused by particle interference in a traditional method is avoided, effective components of extract liquid are not affected by filtration, and a'micro-filtration membrane double-cavity dynamic co-culture system' is constructed; the slow release process of the material in tissue gaps after clinical injection is simulated, and dynamic monitoring of aging toxicity is achieved.

The utility model relates to the technical field of laparoscopes, in particular to a threaded trocar capable of expanding an abdominal wall for a laparoscope, which comprises a puncture sheathing canal, a puncture device inner core is arranged in the puncture sheathing canal, an adjusting sleeve is fixedly connected to the top of the puncture sheathing canal, an air inlet pipe is fixedly connected to the outer side of the adjusting sleeve, and an air outlet pipe is fixedly connected to the outer side of the air inlet pipe. The bottom of the puncture sheath tube is fixedly connected with an expansion balloon. In clinical application, a patient selects a proper operative position, the whole set of puncture sheath tube with the blunt puncture inner core is punctured into the position, needing to be expanded, in the body, a pneumoperitoneum machine delivers medical CO2 gas or an injector delivers clean air into the air inlet tube, and the air inlet tube is communicated with the puncture sheath tube through a gap between the puncture sheath tube and the puncture device inner core. The gas is conveyed into the expansion balloon, and the expanded expansion balloon plays a role in expanding the tissue space, so that the established space is large, the operation is convenient, and the potential safety hazard is reduced.

Provided herein is a cell macroencapsulation device composed of hydrogel in a 3D conformation that optimizes encapsulated cell viability and function when transplanted into a vascularized tissue space. The hydrogel macroencapsulation device is intended to reduce or eliminate immune response to the cell graft, while allowing exchange of encapsulated cell-secreted products, such as insulin. Also described herein is an injection-mold and fabrication process to generate the hydrogel macroencapsulation devices for use in the clinic.

The invention discloses a high-resolution and low-cost tissue space DNA methylomics sequencing method, and relates to the technical field of molecular biology, and the method comprises the following steps: sequentially carrying out cross-linking immobilization, cell permeabilization and transposase transposition on a tissue slice; then tightly covering the micro-fluidic chip on the tissue slice, respectively injecting adaptive sequences containing DNA codes into channels in the X-axis direction and the Y-axis direction of the micro-fluidic chip, adding bar code marks for nucleic acid of the tissue slice, and further combining to form a spatial position coordinate mark with an XiYj form; and then carrying out anti-crosslinking, DNA methylation conversion, library building sequencing and data analysis, and finally successfully obtaining DNA methylation site information of different positions of the tissue slice. According to the method, high genome coverage can be realized by only needing 5X low-depth sequencing data, a highly methylated sample area can be accurately indicated, and the method has the remarkable advantages of low cost and high accuracy, and shows a wide application prospect in the field of biomedicine.

A tissue traction device with a pair of legs biased with respect to each other by a biasing section. The legs may be biased apart and engaged with tissue sites to be separated from each other upon the legs being biased apart from each other. Alternatively, the legs may be biased together, and one leg engaged with tissue cut with respect to a treatment site and the other leg engaged with anchoring tissue spaced apart from the treatment site. The legs are biased together to draw the tissue cut with respect to the treatment site away from the treatment site and towards the anchoring tissue. The tissue traction device may be included in a tissue traction system having a delivery shaft configured to deliver the tissue traction device to a treatment site.

Provided herein are compositions, kits, and methods for collection, integration, and analysis of spatially based information from tissues at the cellular or subcellular level. Layers of information include, without limitation, gene expression, morphology, nucleic acids, and proteins. In some workflows, light-based technologies are incorporated for selective spatial barcoding of regions. In further steps, such barcoded tags are optionally analyzed by high throughput Next Generation Sequencing.

This invention relates to the field of endoscopic image analysis technology, specifically to an intelligent recognition and lesion localization system for digestive endoscopy images. The system includes an endoscope posture analysis module, an image posture matching module, a mucosal texture displacement module, an image rotation correction module, and a lesion coordinate localization module. Based on an electronic endoscope, it analyzes the corresponding sampling times recorded by the voltage of the angle detector of the endoscope's steering control unit and compares the continuity of preceding and following sampling segments. This invention constructs a sequence of lens posture changes by analyzing the voltage trend of the angle detector, pairs the posture records with the acquisition sequence to form an association identifier, and combines this with the tracking of the mucosal texture displacement vector to evaluate the lens steering relationship. Using this as a reference, it calculates the viewing angle offset compensation amount and corrects the cavity wall center position. Furthermore, it adjusts the contour connection relationship of adjacent frames within the chromatic transition region, effectively overcoming the field of view variations and tissue spatial misalignment defects caused by endoscope movement, and ensuring the baseline stability of boundary mapping in continuous images.