131 results about "Brain gliomas" patented technology

The invention relates to the field of medical image processing and pathological diagnosis, aims to solve the problems that brain glioma multi-modal image fusion precision is low, heterogeneity feature and pathological association is weak, and visualization readability is poor, and provides a visualization method and device. The method comprises the steps of multi-modal image preprocessing and registration, regional adaptive multi-modal fusion, pathology association type heterogeneity feature quantification, multi-dimensional pathology visualization, and result verification and optimization. Images are rigidly registered and aligned through mutual information, features are enhanced by adopting a regional differentiation attention fusion algorithm, features are quantified through an improved LSSVM, pathological gold standards are associated, and a three-dimensional visualization model is constructed to visually present heterogeneity information. The device comprises a memory and a processor and can execute the method. According to the method, the heterogeneity evaluation accuracy and clinical readability are improved, treatment scheme optimization is assisted, and the method is suitable for accurate diagnosis and treatment scenes of brain glioma.

The invention provides a multi-mode nuclear magnetic resonance image glioma segmentation method and application thereof, and belongs to the field of medical image processing. The invention provides an M2ES-UNet network aiming at the problems of insufficient utilization of spatial features, single multi-modal fusion mechanism and cross-level semantic loss of an existing segmentation method. According to the method, multi-view anatomical information is extracted through a multi-plane feature collaboration module; utilizing an orthogonal dimension fusion convolution and modal introspection-collaboration module to respectively realize differential fusion of shallow and deep features; and the progressive jump transmission of the features is realized through a coding information smooth transmission module. According to the method, multi-plane and multi-mode complementary information can be effectively mined, the boundary precision and robustness of brain glioma segmentation are remarkably improved, and clinical diagnosis is assisted.

The invention relates to the technical field of biological information, in particular to a brain glioma microenvironment formation key molecular mechanism analysis method, which comprises the following steps: calling expression data to analyze a cell marker sequence to construct a segmentation interval, calculating a candidate factor expression direction to judge trend consistency, identifying expression aggregation difference to construct a split node section, and constructing a subsection; according to the method, partitions are constructed on the basis of marker expression syn-position, judgment is carried out in combination with candidate factor expression directions and marker trends, factor collaboration features are constructed according to the number of trends consistent times, and the semantic sorting information is generated by analyzing the channel change trends to generate drift scores, evaluating multi-label output stability screening key factors and analyzing literature word order positions. Identifying and expressing an aggregation and split structure, guiding a functional pathway to perform trend analysis in a scoring interval and construct a dynamic trajectory, judging label stability and empowerment according to pathway scoring difference, and determining a factor semantic position in combination with a literature word order structure to realize integrated support of regulation and control information and literature evidence.

The invention relates to a planning method, device and equipment for treating brain glioma through multi-fiber laser ablation. The method comprises the following steps: according to pre-acquired image information of a target patient and a segmentation result of a tumor region, sequentially performing segmentation sampling processing on the image information to obtain a candidate optical fiber path set; screening the candidate optical fiber path set according to an optical fiber planning objective function and an optical fiber planning constraint condition to obtain an intermediate optical fiber path set, and determining the number of optical fibers required by tumor ablation according to the intermediate optical fiber path set; determining a target optical fiber path set from the intermediate optical fiber path set according to the number of optical fibers required by tumor ablation and a predetermined path evaluation score; the target optical fiber path set at least comprises position information corresponding to each target optical fiber path. The method can improve the tumor ablation rate and reduce the use of optical fibers.

The invention relates to the technical field of biological medicine, in particular to a nano-vesicle for targeted brain glioma metabolic immune remodeling and a preparation method and application thereof. According to the nano-vesicle for targeted brain glioma metabolism immune remodeling, double-target metabolism and cascade responsive cleavage site modified biological vesicles are integrated, and effective penetration of a drug blood brain barrier and precise targeting of brain glioma can be achieved. The ROS responsive nanoparticles II are prepared by selecting BSA as a drug carrier, applying a nanoprecipitation method and utilizing SLC1A5, LDHA antagonists and responsive linkers, so that the metabolic double-target inhibitor only responds to specific ROS of tumor cells, and specific release in the tumor cells is realized. The cascade response polypeptide provides a simple and convenient delivery scheme with low invasiveness and application prospects for treatment of brain glioma, and is expected to realize precise and individualized treatment in the field of treatment of malignant glioma.

The invention provides a brain glioma CT-MRI (Computed Tomography-Magnetic Resonance Imaging) multi-modal fusion intelligent grading method and a brain glioma CT-MRI multi-modal fusion intelligent grading system, and relates to the field of medical image processing and intelligent diagnosis, and the method comprises the following steps: obtaining CT image and MRI image data of a brain glioma patient through a medical image acquisition device, carrying out standardized preprocessing on the CT image and the MRI image data, and generating a preprocessed image data set; and spatial alignment is carried out on the preprocessed image data set, CT-MRI registration image data are output, and a multi-scale registration method is adopted for spatial alignment. According to the CT-MRI multi-modal fusion intelligent grading method and system based on the brain glioma, by providing the CT-MRI multi-modal fusion intelligent grading method, the problem that in the prior art, image registration and feature fusion are not accurate is solved. A multi-scale registration method and a weighted combined feature extraction mode are adopted, accurate registration of CT and MRI images and efficient fusion of features are ensured, and therefore the accuracy and stability of a grading model are improved.

The invention relates to a brain glioma grading method based on multi-instance causal characterization learning, and the method comprises the following steps: S1, data preprocessing, S2, feature extraction through ResNet-50, S3, feature coding of high-dimensional feature representations of instances, S4, decoupling of low-dimensional feature representations of each instance, obtaining causal features and non-causal features, and carrying out classification on the causal features and the non-causal features. S5, constructing an adaptive adjacency matrix A, constructing a sparse adjacency matrix A (k) by adopting a Topk neighbor selection strategy, and carrying out information aggregation through a graph neural network to obtain aggregation features; S6, carrying out local feature aggregation on each scanning plane p, calculating a weight weighted p of each scanning plane by utilizing a full-connection network, and carrying out weighted fusion to obtain an aggregation feature; s7, fusing and grading the aggregated features and the features; the method has the advantage of improving the stability and clinical applicability of the grading model.

The invention discloses a brain glioma classification method and device based on multi-mode MRI, and relates to the technical field of image processing. According to the method, multiple tumor sub-regions of the brain glioma are acquired based on a to-be-processed multi-modal MR image, multiple local ROIs are extracted based on the multiple tumor sub-regions, and then the sub-type of the brain glioma is determined based on the multiple local ROIs and a global ROI. The subtype can be determined through the multi-modal MR image, so that on one hand, non-invasive identification of the subtype is realized, and bleeding and infection risks caused by identification of the subtype are avoided; and on the other hand, the classification accuracy can be improved by utilizing complementary information between different modal images and a boundary form between different tumor sub-regions. Besides, through the fine structure features of each tumor sub-region reserved from a plurality of visual angles by the plurality of local ROIs, and in combination with the spatial distribution context information of the whole brain glioma provided by the global ROI, a better balance between the processing precision and the calculation efficiency can be realized.

The application discloses a kind of nanomaterial and its preparation method and application, the nanomaterial includes exosome and liposome;The exosome expresses immune checkpoint, the exosome is derived from brain glioma cell;The liposome is loaded with I type photosensitizer.The application is by PDT, immune checkpoint and brain glioma cell exosome antigen three tubes simultaneously to remodel tumor local immune microenvironment, greatly improve the efficiency of cancer immunotherapy, can activate strong anti brain glioma immune response and overcome immune escape.

The invention relates to a brain glioma auxiliary decision-making method based on terahertz signals. The brain glioma auxiliary decision-making method comprises the following steps: carrying out terahertz spectrum detection on brain glioma of different pathological levels to obtain terahertz spectrum data; converting the terahertz wave spectrum data of each wave band into pixel points, and arranging the pixel points according to a preset sequence to obtain a terahertz wave spectrum image; extracting a characteristic value of the terahertz wave spectrum image; inputting the characteristic value of the terahertz wave spectrum image as a training sample into a deep learning model for training to obtain a brain glioma pathology level prediction model; and predicting the pathological level of the target brain glioma by using the brain glioma pathological level prediction model to obtain a prediction result. According to the method, the terahertz spectrum data is converted into the image, the features are extracted, and the deep learning model is combined, so that key features for distinguishing different pathological levels can be automatically learned from a complex spectrum mode, multi-scale and multi-angle comprehensive analysis is realized, and the accuracy of predicting the pathological level of the brain glioma is improved.

The application belongs to the technical field of medicine production, and particularly relates to a preparation method and application of five deuterium-substituted glucose. The method comprises deuterium substitution of D-methyl glucoside, and demethylation of the deuterium-substituted D-methyl glucoside to obtain a target product, namely [2,3,4,6,6'-d5]-D-glucose. The preparation method of the five deuterium-substituted glucose is simple in process route, convenient to operate, mild in experimental conditions, and the synthesized five deuterium-substituted glucose is successfully applied to the description of glycolysis metabolism in a brain glioma model, which is helpful to the clinical conversion application of metabolic imaging technology.

This invention relates to the field of microfluidic chip technology, specifically to a dynamic microfluidic chip device, its preparation method, and its applications for studying CAR-T therapy for gliomas. The preparation method includes: fabrication of the microfluidic chip; reconstruction of neural tissue; reconstruction of the blood-brain barrier; and simulation of the glioma microenvironment. The dynamic microfluidic chip device includes a microfluidic chip; the microfluidic chip includes, from left to right, a vascular channel, a brain parenchyma channel, a tumor channel, and a subarachnoid channel; each of the vascular channel, brain parenchyma channel, tumor channel, and subarachnoid channel includes a cell culture chamber with an inlet on one side; the cell culture chambers of the vascular channel and the subarachnoid channel have corresponding outlets on the other side. The applications include: CAR-T cell infusion; observation of transmembrane migration; and detection of biomarkers. This invention, through the dynamic microfluidic chip device, its preparation method, and its applications, aims to achieve more accurate in vitro evaluation of CAR-T cell therapy.

The application belongs to the technical field of biological medicine, and particularly relates to application of a compound Merulin A in preparation of an anti-glioma drug. The application provides application of the compound Merulin A in preparation of the anti-glioma drug. Experiments prove that the compound Merulin A exhibits significant anti-glioma activity, can effectively promote early apoptosis and cell necrosis of glioma cells, and further inhibit proliferation and migration of the glioma cells. Moreover, due to advantages of wide source, simple extraction process, short cycle and low cost, the compound Merulin A has a wide application prospect as the anti-glioma drug.

The application discloses a polypeptide ligand for brain glioma diagnosis and preparation and application of a radioactive molecular probe. The polypeptide targeting a TREM2 protein has the amino acid sequence as shown in the following: HLRKLRKR. The targeted polypeptide designed by the application has the characteristics of small molecular weight, strong blood brain barrier penetration and strong TREM2 protein binding capacity. The radioactive nuclide labeled molecular probe prepared by the application has the characteristics of strong targeting and easy observation as a brain glioma imaging drug, and is convenient for clinical application. The application improves the diagnosis effect of brain glioma and provides a new idea for early discovery and early treatment of brain glioma.

The invention relates to a multi-modal medical image deep learning enhanced segmentation system and a method thereof, belongs to the field of medical image processing, and aims to solve the problem of inaccurate brain glioma target region boundary segmentation. According to the technical scheme, the method comprises the steps that a metabolic domain PET image and a structural domain MRI-T2 image are obtained, and image registration, noise suppression and intensity standardization preprocessing are carried out; glycolysis features and texture anisotropy features are extracted through a double-domain feature decoupler; constructing a graph neural network to generate a metabolism-structure interaction graph; calculating a pathological infiltration confidence coefficient thermodynamic diagram based on the atlas; the thermodynamic diagram is used for self-adaptive radiotherapy dose segmentation, and the dose gradient is dynamically adjusted; and finally outputting the tumor target boundary.

The invention discloses a model, a method, an electronic device and a medium for MRI brain glioma segmentation, and the model comprises a learnable bias field correction module which is used for adaptively estimating and correcting a low-frequency bias field in an original brain glioma MRI image; the multi-resolution feature extraction module is used for carrying out multi-resolution feature extraction and outputting a multi-resolution feature graph group containing different resolution features and shallow layer features; the multi-resolution feature fusion module is used for carrying out feature fusion on the multi-resolution feature map group and recovering relatively high resolution to obtain deep features; and the statistical space gating fusion module is used for receiving the deep layer features and the shallow layer features, performing feature fusion through channel statistical screening and a space attention gating mechanism, and outputting an MRI brain glioma segmented image. The technical problems that an existing brain glioma MRI image segmentation method is generally poor in adaptability, insufficient in boundary continuity, low in fusion efficiency and poor in global and local feature balance are solved.

The present invention belongs to the field of biomedicine technology and specifically relates to the use of a natural product, Oxirapentyn A, in the preparation of anti-glioma drugs. The natural product, Oxirapentyn A, has significant anti-glioma activity and can inhibit the proliferation, migration, and invasion of glioma cells by promoting early and late apoptosis of glioma cells, showing excellent potential as an anti-glioma drug. Furthermore, the natural product, Oxirapentyn A, can be directly prepared through large-scale fermentation of marine fungi or obtained by simple derivatization of the fermentation product, Oxirapentyn B. It has a wide source, a simple production process, a short cycle, and low product cost, and has broad application prospects.

The invention discloses a preoperative brain glioma image segmentation method and system, and relates to the technical field of medical image processing, and the method comprises the steps: receiving brain glioma multi-mode MRI image data, carrying out the preprocessing of the brain glioma multi-mode MRI image data, and obtaining the processed MRI image data; wherein the modality of the multi-modal MRI image data of the brain glioma comprises one or more of T1, T1ce, T2 and FLAIR; the processed MRI image data are input into a pre-trained nnU-SWMama brain glioma segmentation model, a three-dimensional automatic segmentation result of the brain glioma is output and obtained, the three-dimensional automatic segmentation result of the brain glioma comprises a tumor core and an enhanced tumor area, the three-dimensional automatic segmentation result of the brain glioma is exported, and the three-dimensional automatic segmentation result of the brain glioma is obtained. The accuracy of brain glioma segmentation can be effectively improved.

This invention discloses a method for glioma identification with flexible access control and privacy protection, belonging to the field of medical artificial intelligence and privacy computing. The method includes: secure initialization and fine-grained access control configuration; local training and dynamic parameter updates through bidirectional comparative learning on each client, and selection of the optimal model as a reference source based on a dynamic validation set to achieve cross-site parameter alignment; encryption and signing of uploaded parameters, and secure aggregation on the server side to complete global model iterative updates. The fine-grained access control employs attribute-based encryption based on complex attribute predicates to achieve precise authorized access to model parameters, combines symmetric and homomorphic encryption to protect privacy during transmission and aggregation, and provides verifiability through digital signatures. This invention can achieve stable and rapid convergence in both IID and non-IID scenarios, improving classification accuracy and efficiency, while effectively reducing the risk of leakage in parameter interaction and storage.

The invention discloses a combined application of a PARP7 inhibitor or a pharmaceutically acceptable salt thereof and temozolomide, which is characterized in that the PARP7 inhibitor is used for recovering an I-type interferon signal channel and promoting T cell-mediated immune response and polarization of TAMs to M1 type, so that the side effect of temozolomide is reduced, the sensitivity of temozolomide is enhanced, and an excellent synergistic anti-tumor effect is achieved; the traditional Chinese medicine composition especially has a remarkable curative effect on brain glioma with high malignant degree, and is safe and effective.

The invention provides juglone derivatives and application thereof, and belongs to the technical field of medicines. A series of juglone derivatives are designed and synthesized, and the juglone derivatives can target the HuR protein, inhibit the binding activity of the HuR protein and mRNA and have an obvious inhibiting effect on brain glioma cells. Data of the embodiment shows that the juglone derivative disclosed by the invention has relatively good anti-glioma activity and relatively good safety, the activity of the juglone derivative disclosed by the invention is superior to that of a positive control drug, and the juglone derivative can be used for developing a novel anti-tumor drug and has a relatively good application prospect.

The invention relates to the technical field of medicinal chemistry, in particular to a quinazoline compound as well as a precursor, a derivative and application thereof. The quinazoline compound provided by the invention has excellent precursor chemical stability and in-vivo metabolism stability, has relatively ideal imaging performance in a brain glioma model, and provides more reliable molecular imaging data for accurate diagnosis of cancers such as brain glioma and diagnosis and treatment of neuroinflammation related diseases; good clinical transformation potential is realized.

The invention discloses a brain glioma segmentation method and device based on adaptive spectrum gating and boundary supervision, and a medium, and the method comprises the steps: obtaining a single-mode MRI image of a brain glioma patient, and carrying out the preprocessing, and obtaining a to-be-segmented image; constructing a segmentation model based on the adaptive spectrum gating network; a layered boundary oriented supervision framework is constructed, the framework is used for training the segmentation model, and the supervision framework at least comprises boundary feature alignment loss used for restraining boundary detail reservation on the encoder side and feature-level regularization loss used for restraining feature space separability on the decoder side; and inputting a to-be-segmented image into the trained segmentation model, and outputting a brain glioma segmentation result. According to the method, an encoder-decoder network embedded with a self-adaptive spectrum gating module and a semantic gating fusion module is constructed, and training is carried out by adopting layered boundary guided loss, so that accurate segmentation of a single-mode brain glioma MRI image is realized.

A co-culture model of brain glioma organoids and mouse brain slices, characterized in that the co-culture model is prepared by the following method: (1) taking mouse brain slices, culturing to obtain cultured mouse brain slices; (2) injecting brain glioma organoids into the cultured mouse brain slices obtained in step (1) to obtain brain slices injected with brain glioma organoids; (3) culturing the brain slices injected with brain glioma organoids obtained in step (2) to obtain the co-culture model. The co-culture model of the application can be used to evaluate the invasion phenotype of glioma organoids in vitro and to test and screen brain glioma drugs.

The invention relates to the technical field of nuclear magnetic image auxiliary identification, and discloses a brain glioma boundary identification system based on nuclear magnetic images, which comprises a data input module, the data input module establishes a database based on a plurality of multi-modal MRI data, and normalization processing is performed on the multi-modal MRI data in the database through a data standardization processing module. According to the method, features can be extracted in x, y and z axial directions at the same time by using a 3D convolution kernel, a complete three-dimensional structure of the brain glioma is obtained, a continuous space structure of the tumor is completely captured, one or more 3D bounding boxes can be rapidly output for any input standardized multi-mode MRI through a positioning module, all tumor areas are wrapped with high confidence, and the accuracy of the three-dimensional structure of the brain glioma is improved. Therefore, the identification time of a single sample is shortened from dozens of minutes to a second level, and the boundary of the infiltration area can be clearly and efficiently distinguished through the cascade structure design of positioning and the core area and the infiltration area.

The invention is applicable to the technical field of biological medicine, and provides a self-assembled drug molecule and a preparation method and application thereof, and the preparation method comprises the following steps: synthesizing a compound Py-S-S-COOH; the method comprises the following steps: synthesizing a compound Py-S-S-B1RA; synthesizing a compound A; synthesizing a compound B; synthesizing a compound C; synthesizing a compound D; synthesizing a compound E; synthesizing a compound F; and the compound CBT-Gem-B1RA is synthesized through the synthesis of A chemotherapeutic drug gemcitabine and a targeted peptide bradykinin B1 receptor stimulant are coupled with a peptide fragment with self-assembly performance, so that the molecule has the capabilities of penetrating a blood brain barrier and targeting brain glioma, can smoothly enter a brain glioma part and can be self-assembled into nano particles in brain glioma cells, and the nano particles can be used for preparing a drug for treating brain glioma. The residence time of the drug in tumor cells is prolonged, the drug enrichment concentration is increased, and finally the glioma specific targeted therapy is realized.

The present invention discloses an osthole compound, a metal complex, a preparation method, and applications thereof. The osthole compound (OST-Alkyne) has significant anti-tumor biological activity. OST is further linked to a cyclometallated iridium precursor via a copper-catalyzed azide-alkyne cycloaddition (CuAAC) reaction, exhibiting stronger anti-tumor activity and targeting mitochondria to kill tumors by reducing mitochondrial membrane potential and inducing cell autophagy and necroptosis. The present invention combines natural products with metals, not only achieving good selectivity and anti-proliferation ability for brain glioma cells, but also overcoming toxic side effects on normal cells. This provides a new design concept for novel metallodrugs for treating brain gliomas and has broad application prospects.

The invention discloses a targeted liposome and application thereof, a targeted liposome preparation and a preparation method and application thereof, and relates to the technical field of biology.The targeted liposome is prepared from, by mole, 50%-55% of distearoyl phosphatidylcholine, 30%-35% of cholesterol, 4%-6% of phosphatidyl glycerol, 7%-8% of DSPE-PEG2000 and 0.3%-0.6% of DSPE-PEG2000-targeted peptide, and the targeting peptide in the DSPE-PEG2000-targeting peptide comprises at least one of an ANG2 peptide, a Tf peptide and an ApoE peptide. According to the application, the targeted liposome crosses the blood brain barrier, and efficient release of drug molecules aiming at brain glioma cells is realized.

The invention provides a combined medicine composition for treating brain glioma and application. The medicine composition comprises a copper ion carrier and a targeted NEU1 anti-influenza medicine. According to the combined medicine composition, an effective medicine combination strategy is provided for treatment of the brain glioma, and the combined medicine composition comprises a copper ion carrier, namely the illicit and an anti-influenza medicine, namely the oseltamivir. According to the invention, the condition that the combination of the ilismos and the oseltamivir has a synergistic effect on the treatment of the brain glioma is found for the first time.

The invention discloses a brain glioma sub-target region personalized radiotherapy planning method and system. The method comprises the following steps: inputting a to-be-detected T1C image and a corresponding AAL image into a segmentation model to obtain a brain region segmentation mask file; inputting a to-be-tested T1C image into the mapping model to obtain a pseudo MRS image; inputting the T1C image to be detected and the registered brain glioma labeling file into the habitat model to obtain a habitat map; based on the brain region segmentation mask file, identifying whether the habitat subregion is located in the functional key brain region or not to determine whether a safe dose threshold value is set as a specific tolerance dose corresponding to the functional key brain region or not; and determining the radiation dose of the brain glioma subregion based on the to-be-detected T1C image, the ASL, the pseudo MRS image and a safe dose threshold. Therefore, the stability, repeatability and efficiency of target area sketching can be effectively improved, and biological conformity of radiotherapy dosage can be realized, so that objective and accurate quantitative reference basis is provided for formulating a radiotherapy scheme.