72 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 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 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 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.

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 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 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 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 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.

A breathing mask device for treating brain glioma through suction type atmospheric pressure low-temperature plasma comprises a mask, a catalyst containing channel, a one-way umbrella valve, a discharging channel and an oxygen connector. The invention provides a novel mask device for realizing brain glioma treatment through an inhalation therapy, and the brain glioma treatment can be realized through simple inhalation treatment. In consideration of biocompatibility, a silica gel material is used, and an oxygen input hole is reserved to prevent suffocation. The invention has the beneficial effects of noninvasiveness, high efficiency, multi-mechanism synergistic effect, recurrence risk reduction and comfort.

The present invention provides a method for constructing brain glioma organoids capable of preserving an immune microenvironment. The culture medium, culture system and method provided by the present invention overcome the shortages that, with the prolonging of culture time, a large number of tumor immune cells are lost and the tumor immune microenvironment is greatly damaged in current glioblastoma organoid models, and can more completely preserve the tumor microenvironment of glioblastoma for a longer time, and greatly retain the heterogeneity of organoid tissue. The present invention is suitable for constructing a new generation of glioblastoma organoid models, and provides more reliable research tools for tumor microenvironment research of glioblastoma, tumorigenesis mechanism research, drug efficacy screening, and the development and application of disease models.

This invention relates to an insoluble drug formulation based on dialkylimidazolium biomimetic lipids, its preparation method, and its application, belonging to the field of pharmaceutical formulations. First, an insoluble drug is directly coupled to the primary amine group of the dialkylimidazolium biomimetic lipid or coupled via a linker to form a lipid-drug conjugate. Then, the lipid-drug conjugate, along with other lipid excipients, is dissolved in the dialkylimidazolium biomimetic lipid, and self-assembled in an aqueous phase to form a drug-loaded lipid nanoparticle delivery system with significant drug solubilizing effect. This drug delivery system exhibits good biocompatibility and plasma stability. It can cross the blood-brain barrier and selectively deliver drugs to glioma cells, achieving sustained release of the conjugated insoluble drug in the tumor microenvironment. This provides a new strategy for improving the pharmacokinetic properties of insoluble small-molecule antitumor drugs, including brain targeting, half-life, and bioavailability.

The application discloses a method for establishing a brain glioma grading hybrid network based on multi-view feature fusion, which comprises four steps of data preprocessing, feature extraction, multi-view feature fusion and grading processing of the obtained features through a classifier; the method can balance global receptive field and induction bias, combines global and local multi-scale and multi-granularity features, balances global receptive field and induction bias at the same time, and enhances feature extraction capability; by constructing different view data sources (interlayer view, scanning plane view and modality view) and designing a novel feature fusion module, the network can fully and effectively utilize key information provided by different scanning planes and different modalities of MRI, including interlayer, spatial and multi-modality information, and realizes comprehensive cognition of brain glioma.

This invention relates to the field of medical ultrasound therapy technology, and more particularly to a method and system for controlling ultrasound emission parameters in transcranial acoustic field adaptive correction for the acoustic dynamics treatment of gliomas. It includes: acquiring spatial localization information of the glioma target area and individualized skull characteristics of the patient; pre-compensating the emission parameters of each element of a multi-element phased-array ultrasound transducer based on this characteristic information to generate corrected emission parameters; driving the transducer to emit an ultrasound beam, causing the beam to pass through the skull and form a transcranial focused acoustic field in the target area based on the spatial localization information; applying ultrasound to the target area using this acoustic field in the presence of a sonosensitizer to activate the sonosensitizer and generate an acoustic dynamic effect; monitoring changes in the acoustic field state and / or tissue acoustic properties during treatment, and updating the emission parameters in real time based on the monitoring results to maintain stable focusing. This invention can compensate for acoustic beam distortion caused by the skull, improve the accuracy and stability of transcranial focusing, and achieve adaptive correction during treatment.

This invention discloses a glioma segmentation network based on an anatomical boundary enhancement mechanism, comprising a first feature extraction module for multi-resolution feature extraction of raw glioma MRI images and outputting features; an ABPM module, comprising a T1 edge extraction module, an edge feature embedding module, and an edge-guided recalibration module connected in series; the T1 edge extraction module outputting T1 anatomical edge maps; the edge feature embedding module outputting edge structure embedding features; and the edge-guided recalibration module performing feature fusion and residual connections to output features; a second feature extraction module for further multi-resolution feature extraction; and a feature fusion and output module for inputting the multi-resolution features output by the second feature extraction module, fusing and upsampling them to obtain MRI glioma segmentation images. This invention can achieve high-quality MRI glioma segmentation images even when dealing with weak, filamentous, and discrete punctate lesions.

The application discloses application of a golden orchid glycoside in preparation of a drug product for preventing and treating brain glioma, and in particular, application of the golden orchid glycoside in inhibiting growth of brain glioma. It is found through a cell activity experiment that the golden orchid glycoside can significantly inhibit proliferation of brain glioma cells; the golden orchid glycoside can inhibit growth of intracranial tumors of mice, which indicates that the golden orchid glycoside is expected to be further researched and developed as an anti-brain glioma drug, and further expands the effect of research and development progress of the anti-brain glioma drug.

The application belongs to the technical field of medicine, and discloses a nano-carrier capable of penetrating through the blood-brain barrier and improving brain delivery efficiency, and a preparation and application thereof. The preparation method comprises the following steps: 1) dopamine hydrochloride is deposited on the surface of nanoparticles through an oxidative self-polymerization reaction to obtain polydopamine modified particles; and 2) glucose functionalized polyethylene glycol and end-capped polyethylene glycol are reacted with the polydopamine modified particles to obtain the nano-carrier. The polydopamine coating is used to modify the nanoparticles, and the glucose functionalized polyethylene glycol is used to secondarily modify the polydopamine coating, so that the ability of the nanoparticles to penetrate through the BBB and enter brain tissues and brain gliomas is enhanced. The targeting effect of the glucose functionalized PEG and the synergistic effect of the PDA coating in enhancing cell uptake are combined in the application, so that the brain and brain tumor delivery capacity of the nano-carrier is enhanced. The nano-carrier is used for loading drugs.

The application discloses a brain glioma resection device based on machine vision guidance, which comprises an imaging module, a processing module, a decision module and a resection execution module. The imaging module comprises an optical coherence elastography system and an excitation unit, which is used for applying ultrasonic excitation to brain tissue and acquiring elastic response information of the brain tissue under the excitation. The processing module analyzes the elastic response information, constructs the mechanical property distribution of the brain tissue, and identifies the spatial structure boundary between the glioma tissue and the normal brain tissue based on the mechanical property distribution. The decision module generates a resection path and resection parameters according to the structure boundary. The resection execution module resects the target tissue according to the resection path and resection parameters. By combining the tissue mechanical property information acquired by the optical coherence elastography with the resection control, the controllability and consistency of the resection process are improved, and the brain glioma resection can be assisted or automated.

The invention discloses a brain glioma postoperative recurrence and radiation injury image identification method, which belongs to the technical field of medical image analysis, and comprises the following steps of: standardizing multi-modal image data; reconstructing microvascular metabolism characteristics; analyzing a radiation damage structure; and carrying out combined constraint image identification. Constructing a local perfusion response feature, a diffusion limited feature and an enhanced coupling feature through cross-modal voxel mapping and neighborhood statistical analysis, and forming microvascular metabolism characterization data; meanwhile, radiation damage structure characterization data are constructed in modes of hierarchical structure region division, necrotic cavity distribution modeling, boundary continuity analysis, peripheral tissue disturbance modeling and the like; according to the method, the microvascular metabolism state and the structural damage state of the focus area can be depicted at the same time, and the accuracy and stability of image identification of postoperative relapse and radiation damage of the brain glioma are improved.

The application provides a multi-modal nuclear magnetic resonance image brain glioma segmentation method and application thereof, and belongs to the field of medical image processing.The M2ES-UNet network is provided in view of the problems of insufficient space feature utilization, single multi-modal fusion mechanism and cross-level semantic loss of existing segmentation methods.The method extracts multi-view anatomical information through a multi-plane feature coordination module;orthogonal dimension fusion convolution and mode introspection-coordination modules are used to realize differentiated fusion of shallow and deep features;and the encoding information smooth transmission module is used to realize progressive jump transmission of features.The application can effectively mine multi-plane and multi-modal complementary information, significantly improve the boundary precision and robustness of brain glioma segmentation, and assist clinical diagnosis.

The present application relates to the m 6 Application of methylation level of A as glioma marker, the present application confirms the m 6 The methylation modification level is related to the prognosis condition of brain glioma, and the increase of methylation abundance and expression level means worse prognosis. In addition, the present application researches and confirms that the expression of lncRNA-WEE2-AS1 is related to the formation of drug resistance of anti-tumor drugs, and inhibiting the expression of the lncRNA helps to improve the resistance of dasatinib, and provides a feasible treatment scheme for the clinical targeted treatment of drug-resistant patients.

The present invention belongs to the technical field of biochemistry; and specifically relates to an anti-tumor prodrug, and a preparation method therefor and the use thereof. The anti-tumor prodrug is composed of the tail of nervonic acid and the head of a hydrophilic chemotherapeutic drug. The hydrophilic chemotherapeutic drug is gemcitabine, and the tumor is glioma. An acid-sensitive amphiphilic prodrug compound is synthesized on the basis of using a nutrient nervonic acid capable of penetrating the blood-brain barrier and a chemotherapeutic drug as raw materials. Once the prodrug compound reaches a tumor site, an acid-sensitive bond is automatically cleaved due to the low pH in the tumor microenvironment, causing the prodrug compound to separate into the nervonic acid and the chemotherapeutic drug to perform respective functions thereof, wherein the chemotherapeutic drug kills glioma cells, and the nervonic acid repairs nerve tissues, thereby fundamentally solving the toxic side effects of the chemotherapeutic drug, and integrating therapeutic and rehabilitative effects.

This invention discloses a Fe-based T7 peptide-targeting small-molecule magnetic resonance imaging (MRI) contrast agent for gliomas, its synthesis method, and its applications. The small-molecule MRI contrast agent comprises a targeting peptide and an iron-coordinated pyridine ring contrast agent, linked by a click reaction. The T7 peptide portion of this small-molecule MRI contrast agent can bind to transferrin receptors, allowing for more efficient delivery to the brain for imaging. Compared to traditional Gd-based contrast agents, the MRI contrast agent of this invention exhibits higher specificity and lower toxicity, while also demonstrating better stability, enabling efficient imaging of in situ gliomas.