452 results about "Lymph node" patented technology

The invention relates to the field of medical image analysis, in particular to a deep learning prediction system and method based on multi-modal thyroid cancer lymph node metastasis, and the system comprises a data collection module, a preprocessing module, a nodule segmentation module, a feature extraction module, a feature fusion module, a metastasis prediction module, an interpretability analysis module and a result display module. An ultrasonic image, an elastic imaging image, an ultra-micro blood flow image and clinical index data of a patient are integrated, an improved U-Net algorithm is used for precise segmentation of a thyroid nodule region, a multi-branch deep network is used for extracting multi-modal features, a dynamic weight fusion algorithm is used for integrating the features, and the accuracy of the thyroid nodule region is improved. According to the method, the thyroid cancer lymph node metastasis state (non-metastasis, central region metastasis or lateral neck metastasis) is predicted, meanwhile, a two-dimensional interpretability framework of Grad-CAM activation diagram and SHAP value contribution degree analysis is introduced, an intuitive prediction basis is provided for doctors, and the thyroid cancer lymph node metastasis prediction accuracy is remarkably improved.

The invention discloses a septal medical image segmentation method based on deep learning, and the method comprises the steps: collecting user-defined data sets of different types of medical images, and carrying out the data cleaning, preprocessing and marking, and obtaining a sample data set; wherein the different types of medical images comprise three-dimensional CT images of patients with germ cell tumor, lymphadenoma, neuroma, teratoma and thymoma; the sample data set comprises a 2D slice generated by cutting the three-dimensional CT image and a corresponding mask file; dividing the sample data set into a training set, a verification set and a test set according to a proportion of 8: 1: 1; inputting the training set into a pre-training MedSAM model in batches, and obtaining a prediction segmentation result of voxels in the training sample through forward calculation; determining target loss based on the predicted segmentation result and a prior label corresponding to the mask file, and reversely updating parameters of the pre-trained MedSAM model until convergence to obtain a medical image semantic segmentation model; and evaluating the performance of the medical image semantic segmentation model by using the verification set and the test set.

The invention discloses a breast cancer lymph node metastasis prediction system based on a gene spectrum, and relates to the technical field of breast cancer prediction systems. Comprising a data acquisition module which collects gene spectrum data of a breast cancer patient and collects detailed clinical information of the patient; the preprocessing module is used for carrying out data cleaning and data normalization processing on the collected data; and the feature extraction module is used for extracting principal component features by applying principal component analysis on the basis of the gene expression data. According to the method, gene expression data are considered, various gene spectrum data such as gene mutation and copy number variation and detailed clinical information are integrated, the biological characteristics of the breast cancer can be reflected more comprehensively, and the prediction accuracy is improved.

The invention belongs to the technical field of medicines, and particularly relates to application of circRNA (Ribonucleic Acid) coded protein as a gastric cancer marker. The circRNA disclosed by the invention is provided with an ORF (Open Reading Frame) capable of coding a microprotein circUBE2G1-99aa of 99 amino acids, and the amino acid sequence of the protein is shown as SEQ ID NO: 1. According to the present invention, the designed circUBE2G1-99aa targeting antibody is adopted to detect the expression level of circUBE2G1-99aa in the collected clinical sample, and the expression level of circUBE2G1-99aa in the stomach cancer patient tissue is down-regulated, and is negatively correlated with the large tumor, the lymph node metastasis and the advanced pathological stage of the stomach cancer patient.

Drug delivery nanoparticle compositions and methods for the treatment of a cancer are provided that can be used individually or in combination. These compositions and methods include: (1) exogenous vaccination using neoantigens or tumor-associated antigens to potentiate an anti-tumor immune response; (2) nanoparticle-based drug delivery targeting the liver, aimed at reprogramming the hepatic immune environment to reduce or eliminate tumor antigen tolerance, thereby enhancing the immune system's ability to combat metastatic cancer; and (3) expansion of the liver metastasis treatment platform to target tumor-associated lymphoid structures, supporting the immune response at the primary tumor site. Any of these methods may be optionally combined with chemo-immunotherapy for enhanced efficacy.

The invention provides a papillary thyroid carcinoma lymph node metastasis prediction method, system and equipment. The method comprises the following steps: acquiring a staining collagen image of a first papillary thyroid carcinoma section stained by a hematoxylin eosin staining method; obtaining a multi-photon collagen image of the second papillary thyroid carcinoma slice through a multi-photon imaging method; setting an observation area on the multi-photon collagen image according to the dyed collagen image; extracting collagen characteristics in the observation area; constructing a collagen feature scoring model according to an LASSO algorithm and the collagen features; obtaining a collagen feature score according to the collagen feature scoring model; acquiring clinical risk factors; the papillary thyroid carcinoma metastasis risk information is output according to the clinical risk factors and the collagen feature scores, the collagen features in the papillary thyroid carcinoma microenvironment can be accurately and quantitatively extracted by combining the multi-photon imaging technology and collagen feature analysis, accurate prediction of the papillary thyroid carcinoma metastasis risk is achieved, and the accuracy of the papillary thyroid carcinoma metastasis risk is improved. And the prediction accuracy can be further improved in combination with clinical risk factors.

The invention discloses a novel application of bifidobacterium longum subsp. Infantis CCFM1269 in relieving atopic dermatitis, and belongs to the technical field of microorganisms. The bifidobacterium longum subsp. Infantis CCFM1269 provided by the invention can relieve atopic dermatitis, and is specifically reflected in that (1) ear swelling and back skin thickening of mice are relieved, dermatological symptoms are relieved, and mast cell infiltration is reduced; (2) reducing the total IgE level of serum; (3) the Treg level of mesenteric lymph nodes and the ILA content of serum are improved; (4) IL-4 and TSLP levels in skin tissues are reduced, IL-10 is improved, and Th1 / Th2 immune balance is recovered; (5) improving AHR and CYP1A1 in the skin tissue; and (4) high yield of ILA.

The invention relates to the field of image analysis, in particular to a colon cancer lymph node metastasis risk prediction method and system based on image analysis. The method comprises the following steps: acquiring a pathological section scanning image, carrying out layer-by-layer downsampling and feature reconstruction, and generating pathological section topological features; carrying out adaptive clustering analysis and multi-parameter fusion evaluation on the basis of the pathological section topological features to obtain independent migration evaluation values of individual tumor cells; performing tumor metastasis branch analysis according to the independent migration evaluation value, and constructing a metastasis branch path distribution diagram; extracting an immunohistochemical staining image of a patient to obtain immune cell space distribution data; and performing multi-region lymphatic metastasis simulation and metastasis risk situation prediction based on the immune cell spatial distribution data and the metastasis branch path distribution diagram to obtain a final metastasis risk assessment result. According to the method, the lymph node metastasis risk is accurately and efficiently analyzed, and the credibility of a pathological analysis result is improved.

The invention provides a tumor cell vaccine as well as a preparation method and application thereof. The preparation method comprises the following steps: acquiring and culturing tumor cells; enabling the cell membrane of the tumor cell to express a targeting antibody by utilizing a gene vector infection or gene editing technology, and enabling the targeting antibody to be used for enabling the tumor vaccine to be combined with the dendritic cell; performing overexpression of tumor specific protein on the cell membrane surface of the tumor cell by using a gene vector infection or gene editing technology; a nucleic acid substance of a target protein is wrapped by lipid nanoparticles to form a vaccine framework, and the nucleic acid substance can enable target cells to express directional chemotactic molecules; wherein the directed chemotactic molecule is used for transferring the dendritic cells to lymph nodes; the target vaccine is obtained by wrapping the vaccine framework with the cell membranes of the tumor cells, lymph node homing of the dendritic cells can be promoted, and the antigen presentation efficiency is increased and the immune effect is enhanced through tumor specific protein carried by the target vaccine and assisted by an immunologic adjuvant.

The invention relates to the technical field of image processing, and discloses a breast lump image processing and classification method and system, and the method comprises the steps: collecting breast lump image data, and carrying out the spatial registration and fusion of breast lump images; converting the two-dimensional slice image stack into a three-dimensional model, and detecting and marking a suspicious lump area according to the three-dimensional model; and separating the lump area from the background, and displaying the segmented lumps. According to the method, imaging data of two-dimensional ultrasound, CT or MRI and the like are converted into an intuitive three-dimensional model through three-dimensional reconstruction, a doctor can observe a lump and the relation between the lump and surrounding tissues from any angle, and more comprehensive space information is provided. The three-dimensional reconstruction can more accurately measure lump data, is beneficial to improving the benign and malignant judgment precision, and carries out refined reconstruction on the lump edge, so that a complex boundary which is difficult to identify in a two-dimensional image becomes clear, and is beneficial to evaluating the wettability of the lump and the invasion condition of surrounding blood vessels and lymph nodes.

The present application relates to a lymph node metastasis staging prediction method and apparatus, a computer device, and a storage medium. The method comprises: collecting a CT image and a digital pathological image of a patient; performing depth feature extraction on the CT image and the digital pathological image by means of a ResNet50 network and a ViT network, respectively, to obtain a first feature vector of the CT image and a second feature vector of the digital pathological image; combining the first feature vector and the second feature vector to obtain a fused feature vector; and inputting the fused feature vector into a fully connected neural network, and outputting a lymph node metastasis staging prediction result of the patient by means of the fully connected neural network. The present application uses a depth feature fusion strategy for a CT image and a digital pathological image to construct a lymph node metastasis staging prediction model, such that the generalization of the model can be improved while the prediction precision of the lymph node metastasis staging prediction model is improved.

Techniques for segmentation include determining an edge of voxels in a range associated with a target object. A center voxel is determined. Target size is determined based on the center voxel. In some embodiments, edges near the center are suppressed, markers are determined based on the center, and an initial boundary is determined using a watershed transform. Some embodiments include determining multiple rays originating at the center in 3D, and determining adjacent rays for each. In some embodiments, a 2D field of amplitudes is determined on a first dimension for distance along a ray and a second dimension for successive rays in order. An initial boundary is determined based on a path of minimum cost to connect each ray. In some embodiments, active contouring is performed using a novel term to refine the initial boundary. In some embodiments, boundaries of part-solid target objects are refined using Markov models.

The present invention relates to a novel lipid compound for tissue-specific delivery, and a lipid nano-particle (LNP) composition comprising the same, the lipid nano-particle comprising a modified lipid compound as a component, according to the present invention, internal active substances are selectively delivered into cells of specific tissues such as lymph nodes, spleen, retina, cancer, brain, liver and the like in vivo, thereby preventing side effects and safely exhibiting a desired level of effect. The tissue-specific non-viral LNP delivery vectors can be effectively used for prevention of infectious diseases and treatment of rare and refractory (hereditary) diseases (diseases which are effectively and selectively delivered to in-vivo targeted sites, such as macular degeneration, diabetic retinal degeneration, hereditary retinal degeneration, cancer, cerebral diseases, liver diseases and the like).

A method of generating an isolated population of non graft versus host disease (GvHD) inducing cells comprising a central memory T-lymphocyte (Tcm) phenotype, the cells being tolerance inducing cells and / or endowed with anti-disease activity, and capable of homing to the lymph nodes following transplantation is disclosed. The method comprising: (a) providing a population of at least 70% memory T cells; (b) contacting the population of memory T cells with an antigen or antigens so as to allow enrichment of antigen reactive cells; and (c) culturing the cells resulting from step (b) in the presence of cytokines so as to allow proliferation of cells comprising the Tcm phenotype. Cells generated by the method, pharmaceutical compositions and methods of treatment are also disclosed.

Provided in the embodiments of the present disclosure are a target object identification method, an object identification model training method, a method for detecting a visible lymph node in a CT image, a computer aided diagnosis method, an electronic device, a storage medium, and a program product. The target object identification method comprises: determining an image to be subjected to identification; and inputting said image into an object identification model for object identification, so as to obtain a target object in said image, wherein the object identification model is trained by means of a target sample object, which is identified from a sample image, and a sample label of the sample image, the target sample object is determined from among a plurality of candidate sample objects by means of object type identification results and object position detection results of the plurality of candidate sample objects, the object position detection results are obtained by means of performing position detection on object positions of the plurality of candidate sample objects, and the plurality of candidate sample objects are obtained by means of performing object identification on the sample image.

A contrast agent for imaging lymph nodes or lymphatic vessels with near-infrared light that can selectively image primary lymph nodes relative to secondary or higher lymph nodes. A dose of the composition suitable for imaging lymph nodes or lymphatic vessels.

Immunogenic compositions that include an adjuvant formed from nanoparticles of saponins combined with cholesterol and phospholipids have particular interactions with body tissue and immune cells. These interactions include increased immune response in the draining lymph node, strong activation of T and B cells in the lymph node, and rapid clearance of the saponin fraction while the cholesterol fraction remains in the system.

This invention discloses a method for constructing a biological model of tertiary lymphoid structures, which can rapidly and efficiently induce the generation of in situ TLS, without relying on specific tissues or organs, and without the need for complex and expensive preparation processes such as cell editing and transgenic mice. It can generate stable, mature, and long-lasting TLS structures within 2 to 3 weeks, and has the characteristics of short cycle, low cost, simple operation, reproducibility, safety and effectiveness.

An isolated cell having a central memory T-lymphocyte (Tcm) phenotype, the cell being tolerance-inducing cell and capable of homing to the lymph nodes following transplantation, the cell being transduced to express a cell surface receptor comprising a T cell receptor signaling module is described. Methods of generating same and using same are also described.

The invention discloses a lymph node detection method, system and equipment based on anatomical environment perception and a medium, belongs to the technical field of artificial intelligence medicine, and aims to solve the technical problem of low accuracy of lymph node detection. Comprising the steps of acquiring samples and label data, constructing and training a lymph node detection model, and performing real-time lymph node detection. The lymph node detection model comprises a feature extraction network, a cross-slice attention sub-module guided by a central slice, a three-dimensional channel and space significance enhancement sub-module, a hybrid encoder and Transform decoder sub-module and a detection head sub-module; an auxiliary anatomy environment sensing module is adopted to constrain prototype consistency and difficult negative samples; the auxiliary anatomy environment perception module comprises an ROI-level double-view data enhancement sub-module, a prototype consistency constraint sub-module and a difficult negative sample comparison sub-module. The method can enhance the distinguishing ability of the model to the lymph node and the complex anatomical environment where the model is located, and the lymph node detection accuracy is higher.

The invention discloses a glucan modified bionic nanoparticle as well as a preparation method and application thereof. Preparing nanoparticles based on PLGA as an inner core; the tumor cell membrane is extracted from B16F10 melanoma cells and is used as an outer capsid; glucan is coupled with a hydrophobic long chain through reductive amination and is modified on the surface of a biomimetic nanoparticle cell membrane through a hydrophobic effect. The dextran nano delivery system is successfully constructed, cell membrane specific antigens and dextran are efficiently and jointly delivered to lymph nodes, tumor cell membranes provide epitopes for immune activation of nanoparticles, and beta-dextran provides molecular support for nanoparticle targeting immune cells. In-vivo and in-vitro activity experiments prove that the glycolipid modified bionic nanoparticles can effectively activate innate immunity through macrophages and NK cells, can promote dendritic cell antigen presentation, and initiate potent adaptive immune response. The bionic nanoparticles have huge development potential of novel tumor vaccines, and a new thought is provided for immunotherapy of cancers.

The invention belongs to the technical field of biomedical functional dyes and probes, and relates to a fluorescent probe, in particular to a cyclodextrin-based near-infrared fluorescent probe, which is a conjugate formed by coating a fluorescent molecule with cyclodextrin. According to the near-infrared fluorescent probe disclosed by the invention, cyclodextrin is nested on a carbon chain of a fluorescent molecule, so that the water solubility, histocompatibility and safety are improved, the fluorescence lifetime is prolonged, the light stability is improved, and rapid transmission and response of ureters, lymphatic vessels and lymph nodes in vivo are realized; the method can be used for NIR-I and NIR-II imaging; a near-infrared fluorescence probe with targeting property can be formed by marking various tumor targeting molecules, so that accurate targeting fluorescence imaging of different types of tumors such as urogenital tumors, head and neck cancers, breast cancers and cervical cancers and metastatic lymph nodes in NIR-I and NIR-II windows is realized, and the near-infrared fluorescence probe can be applied to fluorescence imaging navigation in clinical operations.

The invention belongs to the technical field of biological medicines, and provides a lipidosome nano vaccine taking glycyrrhiza polysaccharide as an adjuvant, which comprises a lipidosome shell consisting of phospholipid and cholesterol, and glycyrrhiza polysaccharide and mode antigen OVA257-264 entrapped in a lipidosome core. The invention also provides a preparation method of the liposome nano vaccine taking glycyrrhiza polysaccharide as the adjuvant. The invention also provides an application of the liposome nano vaccine in preparation of antitumor drugs. The lipidosome nano vaccine taking glycyrrhiza polysaccharide as the adjuvant can target lymph nodes, induce an organism to generate cellular immune response and form immune memory, so that the immunotherapy effect on tumors is remarkably enhanced. As a vaccine adjuvant, glycyrrhiza polysaccharide not only has the advantages of being good in biocompatibility, safe and non-toxic, but also can effectively make up for the limitation that a current tumor vaccine adjuvant is low in efficiency, high in toxicity and insufficient in Th1 response.

The invention provides application of lactobacillus rhamnosus GOLDGUT-M520 or a composition of the lactobacillus rhamnosus GOLDGUT-M520 in preparation of a product for enhancing the immunity of an organism. The lactobacillus rhamnosus GOLDGUT-M520 has the function of enhancing the immunity in both a viable form and an inactivated form of the lactobacillus rhamnosus GOLDGUT-M520. The lactobacillus rhamnosus GOLDGUT-M520 has a good antibacterial capability on escherichia coli and staphylococcus aureus which are pathogenic to intestinal tracts, and is beneficial to enhancing a chemical barrier in an innate immune barrier; the strain can improve the phagocytic activity of macrophages and enhance the innate immune response; the strain can improve the T cell proportion, the B cell proportion and the immune globulin level in mesenteric lymph nodes of immunocompromised mice, and can enhance adaptive immune response. The lactobacillus rhamnosus GOLDGUT-M520 can comprehensively play a role in enhancing immunity, and meanwhile, the inherent immune barrier, the inherent immune response and the adaptive immune response are enhanced.

The invention provides a method and a model based on transcriptome and intratumoral microorganisms, and particularly relates to a calculation model for evaluating cervical cancer lymph node metastasis risk and a related method, program, system and equipment. According to the model, abundance information of various gene markers (including CARD9, MNX1AS2, MRAS, OLFML2A and RPS28) and abundance information of multiple microorganisms (including Dialister, Catonella and Campylobacter) are fused as input characteristics, and a risk prediction model is constructed through a machine learning algorithm, so that quantitative evaluation on the lymph node metastasis risk of the cervical cancer patient is realized. The prediction precision of cervical cancer lymph node metastasis is improved, an auxiliary diagnosis decision basis is provided for clinic, and the method has important medical application value and industrial transformation potential.

The invention discloses an application of glucopyranose sulfate galactooligosacharide in preparation of a medicine for treating atopic dermatitis. The glucopyranose galactooligosaccharide can significantly improve the skin condition of a DNCB-induced atopic dermatitis mouse model, including reducing the skin inflammatory factor level, improving the skin barrier function, and reducing the lymph node quality and the number of lymphocytes, and has no abnormal influence on blood routine. The composition has the advantages of small drug application dosage, low pharmaceutical cost, safety, no side effect, strong drug effect and the like, and has a wide application prospect.

The invention discloses an mRNA (messenger Ribonucleic Acid) vaccine of varicella-zoster virus as well as a preparation method and application of the mRNA vaccine. According to the mRNA vaccine provided by the invention, a gE antigen sequence is subjected to codon optimization; then selecting a proper promoter, 5 'UTR, 3' UTR and 3 '-terminal PolyA to combine into complete mRNA nucleic acid for coding the varicella-zoster virus, screening to obtain two groups of mRNA nucleic acid for coding the varicella-zoster virus with both expression level and stability, and adopting a novel cationic lipid imidazopyridyl lipid compound to prepare a vaccine liposome. The targeting property in drainage of lymph nodes is improved, and the humoral immunity and cellular immunity levels excited by the vaccine in animal bodies are remarkably improved.

The invention belongs to the technical field of medical image processing, and particularly relates to a non-functional pancreatic neuroendocrine tumor preoperative index prediction method and system. According to the method, on the basis of abdomen CT image lesion segmentation, pathological grading (low risk / high risk) and lymph node metastasis (LNM) risks are jointly predicted through a multi-task deep learning framework; according to the method, tumor morphological features, radiomics features and clinical parameters (such as tumor size and position) extracted by a segmentation network are combined, and high-precision prediction of preoperative key indexes is realized by using a feature fusion module and a lightweight classification head; through a cross-modal attention mechanism and multi-center data verification, the model achieves the pathologic classification AUC of 0.75 on an internal verification set, achieves the LNM prediction AUC of 0.78, and is significantly superior to traditional clinical experience judgment. According to the method, reliable decision support is provided for preoperative precise operation planning and personalized treatment.

The invention discloses a bladder cancer lymph node metastasis derived tumor organoid culture medium, a culture method and a rapid drug detection method, and belongs to the technical field of organoid and cell culture. The method comprises the following steps: culturing a bladder cancer lymph node metastasis-derived tumor organoid by using a bladder cancer lymph node metastasis-derived tumor organoid culture medium, immersing matrigel drops in the culture medium, and obtaining the bladder cancer lymph node metastasis-derived tumor organoid after the cultured organoid grows to 100-150 microns, to-be-detected drugs with different concentrations are directly added into the cell pore plate inoculated with the tumor organoids, the influence of the to-be-detected drugs on cell growth or metabolism is analyzed through a full-view bright-field imaging method, and the detection result of the drug for treating bladder cancer lymph node metastasis is obtained. Compared with a traditional ENR organ-like culture medium, the method has the advantages that the effect is better, and the cost is lower; the kit is used for screening medicines for bladder cancer lymph node metastasis, is simple and efficient, and can be used for accurately detecting medicines for efficiently killing bladder cancer lymph node metastasis cells.

The invention relates to an early tongue cancer latent lymph node metastasis prediction system, electronic equipment and a storage medium. The early tongue cancer latent lymph node metastasis prediction system comprises a data acquisition module used for acquiring head and neck MRI images, the head and neck MRI images being acquired MRI images of a target object; the automatic tumor detection model is used for performing target detection on the head and neck MRI image to obtain a tumor area image; the lymph node metastasis prediction model is used for performing lymph node metastasis prediction classification on the tumor area image to obtain a lymph node metastasis classification result; by means of the scheme, accurate tumor positioning of early oral tongue cancer can be achieved through the automatic tumor detection model, and accurate prediction of latent lymph node metastasis can be achieved through the lymph node metastasis prediction model.