366 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 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 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.

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 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 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 discloses a cell adhesion protein Nectin-4 targeted bicyclic peptide fluorescent probe and a preparation method thereof, the probe provided by the invention can be used for accurate detection of recurrent tumors and metastatic lymph nodes, and the probe is applied to rapid pathology and fluorescent surgical navigation in an operation. The method has the advantages of high signal-to-background ratio, high specificity and long imaging time window.

The invention relates to a cationized manganese nano adjuvant as well as a preparation method and application thereof. The cationized manganese nano adjuvant comprises manganous-manganic oxide nano particles and cationized template molecules wrapping the surfaces of the manganous-manganic oxide nano particles, the cationization template molecule is a protein subjected to cationization modification treatment, or a protein fragment thereof, or a polypeptide fragment thereof. Precise regulation and control of surface charges and structures of the manganese nanoparticles are realized through a biomineralization technology, and the key problems of a traditional vaccine adjuvant in the aspects of antigen delivery, immune activation and biocompatibility are effectively solved. The cationized manganese nano adjuvant can efficiently adsorb various antigens to form a stable nano vaccine compound, and congenital immunity and adaptive immunity response of an organism can be remarkably activated; lymph nodes can be effectively targeted, the enrichment degree of antigens in the lymph nodes is improved, and the immune effect is further enhanced; meanwhile, the biotoxicity is low, and the stability is excellent.

The embodiment of the invention provides a method and device for identifying lymph node metastasis and prognosis prediction under non-small cell lung cancer carina based on dual-region radiomics and a deep learning model, a storage medium and electronic equipment, and relates to the technical field of lung cancer diagnosis. Compared with a traditional scheme for judging lymph node metastasis of a non-small cell lung cancer patient based on physician experience, an artificial intelligence model is constructed by using dual-region CT radiomics, and a highly reliable prediction result is obtained.

The invention relates to the field of molecular diagnosis and tumor risk assessment, in particular to a marker combination and a kit for assessing the lymphatic metastasis risk of cervical cancer and application of the marker combination and the kit. The marker comprises a group of gene markers and a group of microbial markers, the gene markers comprise CARD9, MNX1AS2, MRAS, OLFML2A and RPS28, preferably, the gene markers further comprise at least one of CFL1P1 and GRASLND, and the microbial markers comprise microorganisms of the genus Dialister, the genus Catonella and the genus Campylobacter. The marker provided by the invention can be used for constructing an accurate risk assessment model, and can be used for developing a corresponding PCR (Polymerase Chain Reaction) kit, a mass spectrum or a chromatographic analysis system to realize metastasis risk prediction, curative effect assessment and new drug screening of cervical cancer patients.

A kit (100) and method (200) are provided for administering an active ingredient (112) to a subject to elicit a response. The kit comprises a first container (110) with an active ingredient (112) and a second container (120) with an adjuvant (122). The containers permit the ingredients to be admixed to form a compounded preparation for delivery into the subject's tissue. The active ingredient can comprise an allergen. The kit can also include temperature control and mixing components, potentially involving a third container (130). The method comprises providing the kit (100), admixing the ingredients, and injecting the compounded form into the subject's tissue such as a lymph node. The injection can be monitored and repeated. The approach can elicit various responses such as desensitization according to the type of active ingredient used. Overall, the kit (100) and method (200) provide targeted delivery of active ingredients to efficiently provoke a localized response in the subject.

This invention discloses an automatic segmentation and scoring method and system for FDG PET-CT lesions in lymphoma, belonging to the field of medical image analysis technology. It aims to improve the segmentation accuracy of lymphoma lesions and the objectivity of the Deauville score. First, standardized uptake values ​​are calculated for FDG PET images, and lymph node morphological features are extracted from CT images based on multi-scale Hessian enhancement filtering to construct a dual-modality PET-CT image pair. Then, a dual-channel depth network is used to extract anatomical structural features and metabolic distribution features respectively. Cross-modal gating fusion is used to suppress physiological uptake interference, outputting preliminary lesion segmentation results. Next, three-dimensional connected component analysis is performed on the segmentation mask, and metabolic heterogeneity index is extracted by combining kurtosis and Haar wavelet multi-scale energy. A graph attention network is used to identify key lesions. Finally, the ratio of key lesions to standardized liver uptake values ​​is combined with the metabolic heterogeneity index to correct the Deauville score, achieving automation from lesion detection to treatment efficacy evaluation.

The invention discloses a thyroid cancer and lymph node accurate positioning system based on three-dimensional imaging, which relates to the technical field of medical image modeling, and comprises the following steps: acquiring two-dimensional images of a cross section and a coronal plane, preprocessing the acquired two-dimensional images, extracting image features of the preprocessed two-dimensional images, and constructing a three-dimensional model based on the image features. Evaluating and adjusting the constructed three-dimensional model, displaying the adjusted three-dimensional model by using a visual tool and providing an interaction tool, generating an electronic diagnosis report based on a three-dimensional model positioning result, storing the generated electronic diagnosis report to a hospital internal database, and performing encryption measures on the access authority of the hospital internal database. According to the thyroid gland and lymph gland positioning method, feature processing is performed based on the preprocessed image, the three-dimensional model is constructed based on the feature processing result, and the adjusted three-dimensional model is displayed by using the visual tool, so that the accuracy of thyroid gland and lymph gland positioning is improved, and the complexity of an operation and the risk of lymph gland omission are reduced.

The invention provides a preparation method and application of a nitrogen-containing heterocyclic micromolecule modified polyethyleneimine polymer derivative. Compared with a polyethyleneimine polymer, the nitrogen heterocyclic ring micromolecule modified polyethyleneimine polymer derivative provided by the invention has immunostimulatory activity. Besides, the polymer derivative material provided by the invention can be simply mixed with a protein antigen to form nanoparticles, so that efficient loading of the protein antigen is realized, the delivery efficiency of the protein antigen to lymph nodes is improved, and the polymer derivative material can be used for preparing a novel nano vaccine preparation.

The present invention provides a method for culturing immune organoids and its application. The present invention cultures immune organoids by isolating lung-infiltrating lymph nodes and evaluates the level of mucosal immune response produced against influenza virus. By adding components such as BAFF and IL-2 to the cell culture medium and using the air-liquid interface method to three-dimensionally reconstruct immune cells derived from lung-infiltrating lymph nodes, immune organoids with a lymphoid structure are formed. These immune organoids can more realistically reflect the level of local immune response in the lungs and better retain various immune cells. At the same time, T cells are activated, enhancing their helper effect on B cells; the time for B cells to differentiate into plasma cells and mature is greatly shortened, and the efficiency of detecting and evaluating the immunogenicity of viral antigens is improved.

This invention provides a method, device, and equipment for preoperative risk stratification assessment of endometrial cancer. It involves segmenting and annotating preoperative ultrasound images to extract radiomics feature vectors, and simultaneously performing structured encoding and mapping of pathological biopsy data to generate pathological feature vectors. These two feature vectors are then input into a fusion network containing a cross-modal attention gating module. This gating module automatically generates dynamic weight vectors based on the consistency between the image and pathological features. The two feature vectors are then weighted and modulated separately before being concatenated to obtain a joint feature representation. This automatically reduces the weight of the lower-confidence modality and amplifies the weight of the higher-confidence modality when their assessment conclusions are inconsistent. Finally, the joint feature representation is input into a multi-task prediction network to output the probability of myometrial invasion depth and the probability of lymph node metastasis risk, thereby generating preoperative risk stratification results and surgical plan recommendations.