14 results about "Tissue space" patented technology

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 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 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.

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 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.

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.

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.

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.

The invention relates to the technical field of mass spectrum detection, in particular to a marker substance spectrum detection method for HCC patient immune microenvironment evaluation. According to the method, mass spectrum data characteristic analysis of each marker is obtained for different tissue space regions, and characteristic indexes are obtained through mass spectrum peak shapes of the markers in tissue region samples; analyzing the incidence relation of characteristic indexes of different markers, and obtaining spatial heterogeneity in combination with an incidence difference expression condition; characterizing the contribution degree of the feature expression of the marker in different tissue region samples by combining the spatial heterogeneity with the significant deviation of the feature index; and obtaining a mass spectrum evaluation characteristic value of the marker through contribution degree adjustment, and carrying out environment evaluation. According to the invention, through mass spectrometric detection of a multi-tissue space sample and combined analysis, the composition state of the immune microenvironment is compared from different space regions, so that the interference of spatial heterogeneity on a marker detection result is reduced, and the real characteristics of the HCC immune microenvironment can be evaluated more comprehensively and accurately.

The invention discloses a 3D tissue reconstruction method and device based on spatial transcriptome data. Comprising the following steps: acquiring space transcriptome data in space-time omics research, and carrying out standardization processing on the space transcriptome data; constructing a space correlation intensity matrix according to the position information of the cells distributed in the tissue space; calculating an initial value parameter of the model, constructing a target model, and determining the weight of a spatial correlation effect constraint space factor; after determining the weight of the initial parameter and the spatial correlation effect constraint space factor, inputting a target model and carrying out iterative optimization to finally obtain an integration result; and finally, performing downstream analysis, such as spatial clustering, differential gene identification and cell development trajectory inference, on the basis of an integration result (a spatial factor), and performing high-resolution reconstruction on low-resolution spatial transcriptome data (the weight of the spatial factor is constrained by the correlation effect of the spatial factor and the space), so that high-precision tissue structure 3D visualization is realized.