242 results about "Tissue sections" patented technology

This disclosure relates to compositions and methods for analyzing a tissue section from a biological sample.

The system and method for processing a digital pathology image using a machine learning model that includes a self-supervised hierarchical Vision Transformer (ViT) configured to perform unsupervised clustering with multiple classification tokens. The method includes receiving a digital pathology image that depicts a tissue slice stained with histological dyes. The digital pathology image may be processed to generate a result comprising multiple predicted classifications of individual patches of the digital pathology image. The result is generated by a machine-learning model using a self-supervised hierarchical Vision Transformer (ViT) that may further comprise a multi-head self-attention module configured to predict a crosspatch relevance metric using an attention mechanism for each individual patch in the digital pathology image thereby assigning the individual patches to a cluster based on the crosspatch relevance metrics.

A platform for characterizing biomolecules, biological tissue sections, viruses, or other particles of interest in a non-invasive, high-throughput, cost-effective, and label-free manner. The platform includes a merged set of imaging modalities including and capable of interferometric microscopy and wide-field or confocal microscopy (such as, for example, a wide-field super-resolution surface enhanced Raman spectroscopy, SERS), although some aspects may be employed with data sets only received from wide-field or confocal microscopy. Related devices, systems, methods, techniques, and variations to the same are also provided.

An automated tape transfer apparatus including a tape feed mechanism feeding a continuous length of an adhesive tape through the automated tape transfer apparatus and a tape applicator applying the adhesive tape to a cutting face of a sample block, wherein a section of the sample block is adhered to the adhesive tape after cutting of the section from the sample block. A slide station transfers the cut section from the adhesive tape to a slide.

The invention discloses a deep learning driven spatial multi-omics data analysis and integration method, and relates to the technical field of cell sequencing, the method comprises the following steps: using each sequencing point location of different tissue slices as a node, using the adjacency relation of each sequencing point location as an edge, and constructing a spatial omics data graph structure; extracting dimension-reduced features from the space omics data graph structure, and embedding the dimension-reduced features as low-dimensional features; adjusting the coordinate information of the low-dimensional features of the different tissue slices through affine transformation so as to perform direction alignment on the sequencing point locations of the different tissue slices; according to the spatial information and the low-dimensional features, matching the sequencing point locations after the directions of the different tissue slices are aligned; and fusing the matched low-dimensional features according to the spatial information to obtain spatial multi-omics data after integration of different tissue slices. According to the method, an efficient analysis, alignment, matching and integration scheme is provided to assist in space omics data analysis and conjoint analysis, so that the accuracy and efficiency of data processing are improved.

Disclosed herein, inter alia, are devices and methods for efficient transfer and analyses of cellular material, tissue samples, such as tissue sections, using carrier substrates and devices.

The invention relates to a method for acquiring a biomarker for assisting CRLM early diagnosis. The method comprises the following steps: acquiring sequencing data, clinical information and metabonomics characteristics of a CRC sample; evaluating and determining a plurality of differentiated machine learning models; performing classification according to the determined machine learning model, and constructing a CRLM biomarker prediction model; screening candidate biomarkers according to the prediction model; and verifying the candidate biomarker at least according to the tissue slice and the serum sample, and determining a final biomarker.

A system for optical interrogation of tissue samples, the system including: a microtome configured to section one or more tissue sections from a tissue block, the one or more tissue sections including one or more tissue samples; a transfer medium configured to gather the one or more tissue sections and to transfer the one or more tissue sections to one or more slides; and an optical interrogation system including an illumination system configured to illuminate the one or more tissue sections and an imaging system configured to perform an imaging analysis on the one or more tissue sections illuminated with the illumination system.

The invention belongs to the technical field of biological medicine, and particularly relates to application of hUC-MSCs combined with Met in preparation of anti-diabetic drugs. The diabetes mellitus is particularly type 2 diabetes mellitus. Through construction of a T2DM rat model, paraffin tissue section detection, serum ELISA, cell experiments and the like, researches prove that T2DM can be effectively treated by HUC-MSCs combined with Met treatment, including reduction of body blood sugar and recovery of blood sugar regulation ability; liver injury caused by T2DM is relieved and repaired; the glycolipid metabolism of the liver of the T2DM rat is improved; the immune inflammation disorder caused by T2DM is relieved; and the apoptosis level of liver cells is improved. And compared with a single hUC-MSCs or Met treatment mode, the combined treatment mode has a better treatment effect. The invention provides a theoretical basis and a new treatment strategy for treatment of T2DM.

The invention provides pathological tissue section dyeing equipment and method. The method comprises the following steps: dividing a to-be-dyed lymphatic tissue section into a plurality of dyeing partitions according to the structural characteristics of lymphocyte tissues in the to-be-dyed lymphatic tissue section; dyeing agents with different concentrations are applied to different dyeing partitions according to the reference light transmittance of the lymphocyte tissue to obtain color gradient vectors of the lymphocyte tissue slices, light absorption characteristics of the dyeing partitions are measured through a photometer, and dyeing characteristics are determined based on the light absorption characteristics and the color gradient vectors; obtaining a dyeing image after dyeing the lymph tissue section, extracting deep features of the dyeing partitions from the dyeing image, and determining a dyeing loss value when dyeing the dyeing partitions based on the deep features and the dyeing features; and based on the dyeing loss value, carrying out self-adaptive adjustment on the dyeing agent concentration of each dyeing zone. By adopting the scheme provided by the invention, optimal adjustment on the concentration of the coloring agent in the sample dyeing process can be realized based on the characteristic difference of the cell tissues at different parts.

The invention discloses a tissue slice high-resolution mass spectrum imaging method and system based on multi-modal data fusion, and belongs to the technical field of mass spectrum imaging and biomedical detection. The method comprises the steps of multi-modal data acquisition, data preprocessing, feature extraction, multi-modal data fusion and high-resolution imaging reconstruction. High-resolution mass spectrum data and high-stability auxiliary modal data are synchronously collected, after preprocessing and feature extraction, feature weights are dynamically distributed and fused by using a fusion model based on an attention mechanism, and then a high-resolution and high-sensitivity imaging result is output through an imaging reconstruction model of a U-Net improved architecture. On the premise that the spatial resolution is not reduced and the sample is not damaged, the mass spectrum signal intensity is effectively enhanced, the low-abundance biomarker recognition capability is improved, the imaging efficiency is high, the adaptability is high, and the method is suitable for biomedical research and clinical diagnosis scenes.

The application discloses a kind of biological tissue section rigidity measurement methods based on photonic crystal colorimetric sensor, the method is according to the set step pitch vertically to photonic crystal colorimetric sensor and is pressed down to tissue section, photonic crystal colorimetric sensor is formed by the elastomer of better biocompatibility and is covered photonic crystal, its Young's modulus can be adjusted according to the rigidity range of tissue section, in the process of pressing down, the different deformation of elastomer due to the rigidity of different regions of tissue, the wavelength of internal photonic crystal reflection changes, that is, the color difference of sensor surface is generated, then the rigidity information of tissue section is obtained according to the color difference analysis.The method realizes the rigidity of biological tissue section fast, simple, high-precision detection, and can be applied to the fields such as tissue engineering, organ chip, clinical examination and the like.

The invention relates to the technical field of pathological section image recognition, in particular to a method for accurately predicting clinical prognosis of a colorectal cancer patient, which comprises the steps of full-view digital section acquisition, full-view digital section preprocessing, Transformer-dynamic agency attention-Mama network and interpretability analysis. According to the method, firstly, a tissue slice segmentation unit and a preliminary feature extraction unit are integrated, full-view digital slice feature extraction is achieved, then a Transformer-dynamic agency attention coupling architecture is adopted, time sequence dependence modeling is achieved through a multi-layer Mama module, finally, a feature aggregation unit is used for outputting patient risk scores, and the patient risk scores are obtained. And realizing visual explanation of the model decision by adopting a thermodynamic diagram. According to the method, through fusion of long sequence data processing, a dynamic agency attention mechanism and a state space modeling technology, the technical bottlenecks of insufficient feature characterization capability, difficulty in long-range dependence modeling and the like in traditional pathological image analysis are solved.

Disclosed herein, inter alia, are devices and methods for efficient transfer and analyses of cellular material, tissue samples, such as tissue sections, using carrier substrates and devices.

The invention discloses a pathological image full-automatic segmentation and classification method and system based on deep learning, and the method comprises the steps: selecting a to-be-processed tissue slice image, extracting the feature information of a cell nucleus in the to-be-processed tissue slice image, and carrying out the initialization processing; the unified feature decoder is introduced to process the image, and different feature information is integrated; carrying out instance segmentation on the cell nucleus by adopting a convolutional neural network, and optimizing a feature extraction process in combination with a self-attention mechanism and a residual network; meanwhile, a classification module is used for carrying out category prediction on cell nucleuses, and tissue region guide information is fused; and carrying out comprehensive optimization on the segmentation result and the classification result through multi-task joint training until a preset stop condition is met, and obtaining a high-quality cell nucleus segmentation and classification result. The method can automatically and efficiently perform cell nucleus segmentation and classification in the digital pathological image, has high accuracy and stability, is suitable for various complicated tissue slice analysis tasks, and does not need manual intervention.

The invention relates to the technical field of medical apparatus and instruments, and discloses an intelligent full-automatic pathological slicing machine which comprises a shell, a sample clamping module, a first mechanical transmission system, a second mechanical transmission system, a waste slice buffering pool, a moving platform and an adjustable cutter module. An inclined conveying mechanism; the slice transfer mechanism and the slice selection mechanism are used for intercepting set tissue slices and applying suction force with a set numerical value to the set tissue slices; the inclined conveying mechanism and the slice transferring mechanism matched with the inclined conveying mechanism are additionally arranged on the adjustable cutter module, the inclined conveying mechanism can automatically convey slices according to the number of the generated slices, and waste slices and slices are cut and separated through the slice transferring mechanism; and then the slices are transferred from the inclined conveying mechanism to the correspondingly arranged slice conveying mechanism, the automatic slice selecting and transferring functions are achieved, tedious manual operation is not needed, and the operation difficulty of an old-fashioned mechanical system slicing machine is reduced.

The application discloses a kind of disease data analysis intelligent chip and system based on multi-modal molecular marker, it is related to disease data processing technical field.The intelligent chip includes: molecular marker image acquisition pre-evaluation module;Molecular marker image generation eligibility evaluation module;Multi-modal molecular marker data eligibility evaluation module.The intelligent chip is in the process of multi-modal molecular marker data analysis, after executing molecular marker image acquisition pre-evaluation eligibility, molecular marker image generation eligibility evaluation is executed, after molecular marker image generation eligibility evaluation eligibility, multi-modal molecular marker data eligibility evaluation is executed, reaches the effect of improving the accuracy of molecular marker disease image group data analysis, solves the problem of low accuracy of molecular marker disease image group data analysis caused by uneven thickness of kidney biopsy frozen tissue section in the prior art.

1. Name of the product in this design: Scanner (2.5mm range spectral line confocal). 2. Purpose of this design: To photograph tissue sections after staining to obtain comprehensive image information. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The utility model provides a full-automatic device for repairing and dyeing tissue slices. A cooling and liquid discharging system is arranged on a frame body of the full-automatic device, a heating block is arranged on the cooling and liquid discharging system, a glass slide frame is arranged above the heating block, and a glass slide is arranged above the glass slide frame; the frame body is also provided with a driving assembly, the driving assembly is connected with a pressing structure and a dragging plate, the pressing structure can press the cover plate, the cover plate and the glass slide are in one-to-one correspondence to form a reaction cavity, and the head of the cover plate is connected with the dragging plate. According to the utility model, the problems of poor dyeing consistency effect, insufficient tissue repair effect and dry sheet are solved.

The utility model belongs to the technical field of biological tissue slicing machines. A histotome shell convenient to disassemble and maintain is characterized in that the shell is mainly composed of a left half shell body, a right half shell body and a tray, the left half shell body is located on the left side of the right half shell body, and a cavity is defined by the left half shell body and the right half shell body; a left half chuck hole is formed in the middle right portion of the front face of the left half shell, a right half chuck hole is formed in the middle left portion of the front face of the right half shell, and the right half chuck hole and the left half chuck hole define a chuck hole. A right hand wheel hole is formed in the right side face of the right half shell, the bottom of the left half shell is an open end, bottom fixing screw holes are formed in the left front end and the left rear end of the bottom of the left half shell respectively, the bottom of the right half shell is an open end, and bottom fixing screw holes are formed in the right front end and the right rear end of the bottom of the right half shell respectively. The tray is located in the left concave face of the left half shell and the right concave face of the right half shell, and fixing inserting rods are arranged at the four corners of the bottom face of the tray respectively and inserted into the corresponding top face fixing holes. The shell is convenient to disassemble.

The invention discloses a pathological image classification method and system for assisting pathological diagnosis, and aims to solve the problems that the traditional pathological diagnosis is low in efficiency and the accuracy depends on artificial experience. According to the method, a pathological image is collected, denoising, enhancement and other preprocessing operations are executed, a pre-trained deep learning model is utilized to automatically extract image features, and classification results of benign, malignant or specific disease types are output in combination with a classification algorithm, so that efficient and accurate automatic diagnosis is realized; the system integrates image acquisition, preprocessing, feature classification and result display modules, supports multi-user concurrent access and cloud deployment, is equipped with model updating and user interaction functions, and can continuously optimize model performance based on new data. In addition, remote pathological diagnosis is supported, and balanced distribution of medical resources is promoted through a digital system; the method can significantly reduce the workload of pathologists, reduces the risk of human misdiagnosis, and is suitable for rapid classification and diagnosis of various pathological images such as tissue slices, cell smears and the like.

The present invention relates to a biomarker for predicting immunotherapeutic responsiveness based on spatial transcriptome analysis and uses thereof and, in particular, to: a marker composition for predicting the responsiveness of cancer patients to immunotherapy; a composition for predicting the responsiveness of cancer patients to immunotherapy; a kit for predicting the responsiveness of cancer patients to immunotherapy, comprising the composition; a method for providing information for predicting the responsiveness of cancer patients to immunotherapy; and a method for providing information for predicting the survival prognosis of cancer patients. The biomarker for predicting immunotherapeutic responsiveness, according to the present invention, was discovered by applying spatial transcriptome technology and analyzing cell group-specific gene expression values according to location information of cells in tissue sections, and can more precisely and accurately predict the responsiveness of cancer patients to immunotherapy and the survival prognosis of patients, thus enabling suitable treatments for patient groups, which may result in improved therapeutic effects and a reduction in pain and costs for patients.

The invention discloses a cigar tobacco tissue thickness measuring method and a cutting device, and relates to the technical field of tobacco tissue thickness measurement, and the method comprises the following steps: S1, carrying out screening treatment before cigar tobacco and rootstock cutting; s2, device cutting module selection and parameter setting work; s3, tobacco plant rhizome and leaf tissue structure slices are cut; s4, tissue slice electron microscope scanning and image selection work; and S5, carrying out tissue slice image part identification and division and average thickness calculation. The method solves the problems that in the prior art, in thickness measurement of different tissue structures of tobacco leaf sections, most methods or leaf surface image analysis systems are used for estimating and reading scale scales, and due to the fact that thickness distribution of tissue parts of leaves is not uniform and consistent, a traditional scale measurement method is large in error due to different selected measurement point positions, and the measurement accuracy is poor. And the average thickness of each part cannot be quickly and accurately measured.

The application discloses a fusion protein, which comprises an antibody binding domain and an RNA editing domain, wherein the antibody binding domain is a pAG domain, which comprises the functional domains of Protein A and Protein G, and the RNA editing domain comprises a rat APOBEC1 enzyme deamination domain and / or a human ADAR2 enzyme deamination domain. The fusion protein can be used to simultaneously identify RBP targets and provide transcriptome information in the same sample, can characterize the RNA-protein interaction spectrum, has high reliability and sensitivity, is suitable for studying the RBP functions in tissue sections and single cells, and has wide application prospects in the functional mechanism research of RBP in the development and disease processes.

The invention belongs to the technical field of deep learning and image classification, and relates to a lung cancer tissue classification method based on improved Swinin-Transformer, and the method specifically comprises the following steps: S1, preparing a data set: collecting lung full-view pathological tissue slice images to construct the data set, which comprises various lung cancer subtype tissue slices and normal lung tissue slices; the method is a multi-example learning method, a self-supervised comparative learning mode is adopted to compare feature differences between positive and negative samples, a double-flow channel structure is adopted to extract features under different amplification factors, each channel takes Swim-Transform as a main network framework, an EMA module is added between Swin Transform Block modules to enhance the capability of extracting multi-scale features, and the multi-scale feature extraction efficiency is improved. And carrying out feature fusion by adopting a feature weighted aggregation mode so as to carry out classification prediction on the whole image. According to the method, the final classification result is returned to the original image and displayed by the thermodynamic diagram, so that the purposes of full-view image cancer classification and focus area detection are achieved.

The present disclosure relates to automated systems and methods for sectioning tissue from biological tissue blocks, and more particularly to systems and methods for providing higher throughput in such automated devices. The present disclosure can implement systems and methods for increasing throughput in automated tissue sectioning devices to reduce tissue sample preparation time for multiple tissue blocks. The present disclosure further provides systems and methods for hydrating tissue samples.

The utility model discloses a tissue slice spreading machine, and relates to the technical field of medical equipment. The device comprises a machine body, the top of the machine body is fixedly provided with a piece bleaching pool and a piece drying machine, the piece bleaching pool is fixedly installed on the left side of the top of the machine body, the piece drying machine is fixedly installed on the right side of the top of the machine body, and a plurality of timing assemblies are vertically arrayed and fixedly installed between the piece bleaching pool and the piece drying machine. Each timing assembly is located on the side edge of one long-strip-shaped slope face of the slice dryer. According to the utility model, after the bleached slices are fished out, the slices are shifted and placed on one long-strip slope surface of the slice drying machine side by side for drying, the timing assembly which is arranged on the left side and is aligned with the long-strip slope surface is controlled to start timing, and then the next group of slices are shifted and placed on the other long-strip slope surface for drying; meanwhile, timing is carried out, so that the tissue slices placed at different times can be independently timed, whether the drying duration of each group of slices is reached or not can be conveniently observed, and convenience is brought to workers.

The application provides a kind of fluorescence image conversion method based on tissue slice, for converting the fluorescence image to be converted of certain channel of tissue slice into the fluorescence image of other channel in same field of view, it is characterized in that, including the following steps: constructing deep learning network Pix2Pix;Loss function is constructed;The data set required for training is obtained;The data set is input to deep learning network Pix2Pix, the deep learning network Pix2Pix is optimized training using loss function and Adam optimization algorithm, and the trained deep learning network Pix2Pix is used as fluorescence image conversion model;The fluorescence image to be converted is input to fluorescence image conversion model, to obtain the fluorescence image of other channel in same field of view corresponding.The fluorescence image conversion method of tissue slice utilizes deep learning technology to find the prediction relationship between different channels of fluorescence image, so as to realize the image conversion of different channels, reduces the time-consuming and laborious work in (multi-label) fluorescence imaging process, saves cost and time.

Alignment of a 2D image to a corresponding 3D image is provided by writing a pattern into a 3D sample. The pattern is at known positions in the 3D image, and provides visible reference features in the 2D image. This permits accurate determination of the plane in the 3D image that corresponds to the 2D image. Improvements in this work relate to : sample preparation, for example direct staining of the tissue with a dye that binds to the tissue; alignment accuracy, for example using patterns and optics that provide better depth information; and use of the 2D images to render a 3D image where the reference features from the pattern enable the 3D rendering.

To provide a pathological condition diagnosis support system and a pathological condition diagnosis support method capable of supporting highly reliable diagnosis of a pathological condition by suppressing clouding and staining of a stretchable sheet.SOLUTION: The pathological diagnosis support system 1 includes an extension device 10 for extending a tissue section P of a cell, an imaging device 30 for acquiring image data by imaging the tissue section P, and an image analysis device 40 for calculating an index of morbidity based on a mode of cracks generated in the tissue section P due to extension by analyzing the image data, and the extension device 10 supports a stretchable sheet 60 provided with the tissue section P and extends the tissue section P together with the stretchable sheet 60. The stretchable sheet 60 has at least the first sheet surface 61 provided with the tissue section P, and the number of micropores having a maximum width of 100 μm or less per predetermined area in the first sheet surface 61 is smaller than that of the polyurethane gel.SELECTED DRAWING: Figure 1