285 results about "Glioma" patented technology

The invention relates to the technical field of boundary recognition, in particular to a glioma boundary recognition method and system based on image fusion, and the method comprises the following steps: obtaining a multi-modal brain image, constructing a fusion matrix, extracting the gray features of an edge region and an adjacent region, recognizing signal-noise abnormal points, and revising a judgment standard. And adjusting the path direction and reconstructing an edge communication structure, and generating a glioma boundary region map under fusion. According to the method, high-precision alignment among modals is realized through multi-modal image gray scale unification and registration processing, key details are expanded and focused by enhancing edges and regions, the recognition accuracy is improved, gray scale comparison between the edges and outer adjacent regions is introduced, the signal distinguishing capability is enhanced, misjudgment is avoided, judgment conditions are dynamically revised according to the signal-noise difference, and the accuracy of recognition is improved. The method enables the recognition standard to have local adaptability, combines the path change trend to reorder and connect edge points, guarantees the continuity of a boundary structure, integrally improves the accuracy and integrity of fuzzy boundary recognition, and enhances the glioma contour extraction effect.

The invention discloses a tumor space-occupying brain network neural image alignment method based on multi-modal fusion, and belongs to the technical field of medical image processing and artificial intelligence crossing. The method comprises the following core steps of multi-modal image heterogeneous feature decoupling, tumor occupation deformation field modeling, functional network topological structure maintenance, cross-modal feature adversarial alignment, dynamic deformation constraint optimization and clinical interpretability verification, and construction of a three-dimensional non-rigid registration network based on a double attention mechanism. And differential homeomorphic mapping of a tumor focus area and normal brain tissue is realized through the cascaded spatial transformation module. Aiming at the problems of insufficient multi-modal feature alignment and brain network topology distortion in the prior art, the invention provides a function connection constrained cross-modal fusion strategy, a graph convolution network is adopted to encode resting state function connection features, and network node displacement caused by tumor occupation is dynamically corrected in combination with deformable convolution and a bidirectional feature competition mechanism; a space consistency loss function based on white matter fiber bundle tracing is designed, and through diffusion tensor imaging feature guide structure-function bimodal joint optimization, the problems of insufficient registration precision in a focus area and whole brain network connection distortion of a traditional method are solved. Experiments show that the registration precision of the method in glioma cases reaches 0.82 mm and is improved by 37% compared with that of a traditional method, and dissection-function consistency of functional network reconstruction around tumors is remarkably improved.

The invention provides a drug screening and curative effect evaluation method and system based on a glioma organ-like model, and relates to the technical field of biological material analys.The method includes the steps that a three-dimensional fluorescence image of the glioma organ-like model is collected, signal intensity analysis and compensation processing are carried out, a growth feature mapping model is constructed, and the three-dimensional fluorescence image of the glioma organ-like model is obtained; and phenotypic characteristics for representing the glioma organoid heterogeneity are obtained. Inputting the phenotypic characteristics into a drug response evaluation network, and calculating a drug sensitivity score; the drug molecular feature library and the drug collaborative analysis model are combined, the synergistic interaction index of different drug combinations is calculated, the optimal drug combination is screened, the drug administration dosage and the drug administration time sequence are determined in combination with phenotypic features, and finally an individualized treatment scheme is generated. According to the invention, the curative effect of the drug on the glioma organs can be effectively evaluated, and the optimal drug combination and administration scheme can be screened out, so that the accuracy and effectiveness of glioma treatment are improved.

The invention relates to a glioma radiotherapy effect prediction method based on habitat analysis imageomics, and belongs to the technical field of medical image artificial intelligence and tumor radiotherapy, and the method comprises the steps: collecting a multi-mode MRI image, a radiotherapy plan computed tomography CT image, a dose image and a local recurrence label before postoperative radiotherapy, and converting the images into a standard format; sequentially performing craniocerebral boning processing, N4 bias field correction, isotropic resampling, gray level normalization processing and image registration on the acquired image to obtain a preprocessed image; based on the planned target volume PTV, dividing the interior of the PTV into three types of physiological structure habitat areas and three types of physiological habitat areas; image omics features of the multi-mode MRI images are extracted in the PTV and all habitat areas, and an optimal feature subset is screened out; taking the screened optimal feature subset as an input, taking the local recurrence label as an output label, and selecting an optimal model; and generating an output result by using the optimal model.

The invention belongs to the technical field of medical image processing, and particularly relates to a glioma segmentation method based on a multimodal fusion network guided by anatomical symmetry, which comprises the following steps of: jointly inputting an FLAIR image, a T2 image, a T1 image and a T1c image of the same glioma into a trained image segmentation model, and outputting a predicted segmentation image by the trained image segmentation model, the prediction segmentation image is a glioma MRI image with three segmentation areas obtained through prediction, and the three segmentation areas are an edema area, an enhanced tumor area and a necrosis area respectively; the image segmentation model comprises an encoder, a jump connection part and a decoder; the encoder comprises an ASG module, the jump connection part comprises an IMP module, and the decoder comprises a CMF module. Through a three-module cooperation mechanism, the performance of tumor localization, cross-modal fusion, subregion segmentation and the like is improved, and a reliable image basis is provided for glioma operation plan formulation, prognosis evaluation and personalized treatment decision.

This invention relates to the field of anticancer drugs, and more particularly to an indole-3-aryl ketone derivative, its preparation method, and its applications. The indole-3-aryl ketone derivative is compound I-VI, which can be used to prepare drugs for the prevention and / or treatment of cancer, including at least one of breast cancer, liver cancer, lung cancer, colorectal cancer, leukemia, pancreatic cancer, glioma, osteosarcoma, cervical cancer, ovarian cancer, kidney cancer, and esophageal cancer. This invention screened the activity of various compounds and, for the first time, discovered that compound I-VI exhibits excellent inhibitory activity against multiple different types of human cancer cells, providing a potential option for broad-spectrum anticancer drugs.

The present invention relates to a hetero (aromatic) ring-substituted cyclic diamine compound, the preparation thereof and the use thereof in the preparation of a drug for treating and / or preventing tumors. The structural formula of the compound is as shown in (I). The compound has a significant inhibitory effect on the growth of cells of tumors such as leukemia, lymphoma, breast cancer, melanoma, ovarian cancer, colorectal cancer, cervical cancer, lung cancer, prostate cancer, esophageal cancer, glioma, kidney cancer, nasopharyngeal carcinoma, liver cancer, gastric cancer and pancreatic cancer. Compared with the prior art, the compound of the present invention has a broad-spectrum anti-tumor activity, has a good effect on inhibiting tumors, and has an effect of degrading PD-L1 proteins, and is thus a PD-L1 immunomodulator.

The invention discloses a glioma radiotherapy postoperative risk assessment method based on a magnetic resonance image, particularly relates to the field of glioma radiotherapy patient health risk assessment, and is used for solving the problem that an existing assessment mode depends on manual interpretation and is difficult to predict bad clinical outcomes in advance. The method comprises the following steps: performing clinical data gridding reconstruction on a corticoid use cycle of a patient and tumor molecular typing, and combining morphological characteristics of an edema region in a magnetic resonance image to generate time-aligned clinical comprehensive characteristic vectors; mining a frequent association item set between the comprehensive feature vector and the pathological process to construct a mapping relation model; establishing a probability graph reasoning model of the bad outcome based on the pathological process vector sequence and the probability weight; and integrating the two types of models to form a causal reasoning network, and inputting a target patient feature vector to calculate an accumulated risk value of reaching a bad outcome. According to the method, an interpretable individual risk assessment result can be output, and a basis is provided for postoperative follow-up visit and intervention.

The invention relates to the field of anti-cancer drugs, in particular to an indole-3-aryl ketone derivative as well as a preparation method and application thereof. The indole-3-aryl ketone derivative is a compound I-VI, the indole-3-aryl ketone derivative can be used for preparing drugs for preventing and / or treating cancers, and the cancers comprise at least one of breast cancer, liver cancer, lung cancer, colorectal cancer, leukemia, pancreatic cancer, glioma, osteosarcoma, cervical cancer, ovarian cancer, kidney cancer and esophageal cancer. According to the invention, activity screening is carried out on various compounds, the fact that the compounds I-VI have excellent inhibitory activity on various different types of human cancer cells is found for the first time, and an optional scheme is provided for broad-spectrum anti-cancer drugs.

The invention belongs to the technical field of glioma image recognition, and discloses a glioma area rapid recognition method and system based on a medical image. The method comprises the following steps: acquiring a multi-modal image sequence of a brain to be detected; performing weighted fusion on the multi-modal image sequence to generate a fused image; performing gray asymmetry analysis on the fused image, and identifying an asymmetry salient region of the fused image; performing texture difference analysis on the fused image, and extracting a high-difference region of the fused image; performing spatial intersection operation on the asymmetric salient region and the high-difference region, and taking a spatial intersection operation result as a glioma suspicious region; and carrying out dynamic region growth segmentation on the glioma suspicious region, and rapidly outputting a glioma region boundary. According to the method, dual feature screening of gray and texture is combined with dynamic region growth segmentation, the tumor infiltration boundary is accurately captured, the positioning precision is improved, and the false positive rate of recognition is reduced.

The invention discloses application of a CXCL14 combined immune checkpoint inhibitor in treatment of spinal glioma. The immune checkpoint inhibitor is selected from a PD-1 inhibitor or a combination of the PD-1 inhibitor and a CTLA4 inhibitor. In-vivo and in-vitro experiments prove that the CXCL14 combined with the PD-1 inhibitor, the CXCL14 combined with the PD-1 inhibitor and the CTLA4 inhibitor can be used for remarkably inhibiting the growth of mutant spinal glioma and have a remarkable synergistic effect. The invention provides a new strategy for treatment of spinal glioma, and is widely applied clinically.

The present invention relates to the use of selective aquaporin inhibitors, e.g., of aquaporin-4 or aquaporin-2, e.g., certain phenylbenzamide compounds, for the prophylaxis, treatment and control of aquaporin-mediated conditions, e.g., diseases of water imbalance, for example edema (particularly edema of the brain and spinal cord, e.g., following trauma or ischemic stroke, as well as the edema associated with glioma, meningitis, acute mountain sickness, epileptic seizures, infections, metabolic disorders, hypoxia, water intoxication, hepatic failure, hepatic encephalopathy, diabetic ketoacidosis, abscess, eclampsia, Creutzfeldt-Jakob disease, and lupus cerebritis, as well as edema consequent to microgravity and / or radiation exposure, as well as edema consequent to invasive central nervous system procedures, e.g., neurosurgery, endovascular clot removal, spinal tap, aneurysm repair, or deep brain stimulation, as well as retinal edema), as well as hyponatremia and excess fluid retention, and diseases such as epilepsy, retinal ischemia and other diseases of the eye associated with abnormalities in intraocular pressure and / or tissue hydration, myocardial ischemia, myocardial ischemia / reperfusion injury, myocardial infarction, myocardial hypoxia, congestive heart failure, sepsis, and neuromyelitis optica, as well as migraines, as well as to novel assays for identifying aquaporin inhibitors.

The invention relates to a hetero (aromatic) ring substituted cyclic diamine compound, preparation thereof and application of the hetero (aromatic) ring substituted cyclic diamine compound in preparation of drugs for treating and / or preventing tumors. The structural formula of the compound is shown as (I). The compound has an obvious growth inhibition effect on tumor cells of leukemia, lymphoma, breast cancer, melanoma, ovarian cancer, colorectal cancer, cervical cancer, lung cancer, prostate cancer, esophagus cancer, glioma, kidney cancer, nasopharynx cancer, liver cancer, stomach cancer, pancreatic cancer and the like. The compound disclosed by the invention has broad-spectrum anti-tumor activity; the compound has a good tumor inhibition effect and a PD-L1 protein degradation effect, and is a PD-L1 immunomodulator.

The invention belongs to the technical field of medical artificial intelligence, and discloses a glioma feature prediction method and system based on multi-modal magnetic resonance and multi-task deep learning, and the method comprises the steps: processing input data through a modal completion module which combines knowledge distillation and modal existence coding, so as to deal with modal deficiency; after deep features are extracted by using a shared encoder, the deep features are sent to a hierarchical task gating prediction network, and the network performs gating modulation on post-stage task features through a pre-stage task result to realize task collaboration; and carrying out model training by adopting a self-adaptive loss function based on sample difficulty. Through the design, the accuracy and robustness of glioma segmentation, molecular marker prediction and lifetime prediction are improved, and the uncertainty score of the prediction result can be provided during reasoning.

The invention discloses a preparation method and application of a colloidal gold immunochromatography test strip for detecting IDH1 mutant protein, and belongs to the field of rapid immunodetection. The colloidal gold chromatography test strip comprises a bottom plate, a sample pad, a nitrocellulose membrane, a water absorption pad and colloidal gold, the nitrocellulose membrane is sequentially provided with a detection line T and a quality control line C in the flowing direction of a sample, the detection line T is coated with IDH1 mutant protein, and the quality control line C is coated with a second antibody corresponding to a monoclonal antibody of the IDH1 mutant protein. Clinical tumor tissue samples collected in real time in glioma resection surgery are treated, an object to be detected and an immune gold label are mixed, incubated and dropped in a test strip sample adding hole, and the object to be detected runs along the test strip under the driving of a buffer solution and generates visual color change. The test strip has the advantages of being rapid in reaction, easy and convenient to operate, convenient to carry, high in specificity and the like, makes up the blank of an IDH1 mutant protein concentration quantitative method, and has good clinical application value.

The invention relates to the field of antitumor drugs, in particular to a thiophene acetyl tetrahydro-beta-carboline compound and application thereof. The thiophene acetyl tetrahydro-beta-carboline compound or the pharmaceutically acceptable salt thereof, and the pharmaceutical composition containing the compound can be used for preparing medicines for preventing and / or treating various cancers. The cancer is at least one of breast cancer, lung cancer, osteosarcoma, melanoma, gastric cancer, pancreatic cancer, kidney cancer, glioma and ovarian cancer. Activity screening is carried out on various compounds, it is found for the first time that the 12 compounds provided by the invention have remarkable broad-spectrum inhibitory activity on various human cancer cell lines, and key candidate compounds are provided for developing novel anti-cancer drugs.

The invention provides methods and compositions for treatment of a subject with a glioma. The methods comprise administering a composition comprising Coenzyme Q10 to the subject by continuous intravenous infusion; and administering radiation therapy to the subject. The composition comprising Coenzyme Q10 may be administered to the subject by continuous intravenous infusion for at least 24 hours before the radiation therapy is initiated.

Disclosed are methods of treating cancer in a subject comprising (i) administering a pharmaceutical composition comprising a therapeutically effective amount of a mutagenized IL 13 moiety (mIL 13); and then (ii) delivering a radiation therapy.

The invention provides an innovative model based on the correlation between the PLEKHA4 gene expression level and the low-grade glioma radiotherapy sensitivity and a prediction method. According to the model, a multi-factor Logistic regression analysis framework is constructed, and the PLEKHA4 gene expression level and key clinical pathological parameters are organically combined, so that a radiotherapy sensitivity prediction model is established. The method comprises a series of steps of sample collection, gene expression detection, clinical information collection, model calculation, result interpretation and the like, and can realize accurate prediction of radiotherapy response of low-grade glioma patients. Compared with the prior art, the method has the remarkable advantages of simplicity and convenience in operation, high prediction accuracy, high clinical transformability and the like. The method has great potential in the aspect of guiding individualized radiotherapy scheme formulation, can significantly improve the treatment effect, and has important value for medical application. Besides, the model can be optimized through further clinical verification, so that clinical practice can be better served, and the life quality and prognosis effect of patients are improved.

The invention belongs to the technical field of medical image processing, and relates to a brain tumor segmentation method based on three-axis context coding, which comprises the following steps: jointly inputting an FLAIR image, a T2 image, a T1 image and a T1ce image of the same glioma into a trained image segmentation model, and outputting a prediction segmentation image with a non-enhanced necrosis region, an edema region and an enhanced tumor region by the trained image segmentation model; the image segmentation model comprises an encoder and a decoder; the encoder comprises a TCE module, and the TCE module adaptively learns relative importance among an axial plane branch, a coronal plane branch and a sagittal plane branch to realize dynamic weighted fusion of multi-plane features. By pertinently solving the problems of insufficient anisotropic feature capture and poor multi-modal fusion adaptability, high-precision segmentation of a non-enhanced necrosis region, an edema region and an enhanced tumor region is realized, and reliable technical support is provided for clinical diagnosis and treatment planning of brain tumors.

The application belongs to the technical field of antitumor drugs, and discloses an iron complex as well as a preparation method and application thereof.The preparation steps of the iron complex are as follows: 1, reacting 2-hydrazinylbenzothiazole, a ketone compound and a solvent to obtain a ligand; and 2, reacting the ligand, ferric chloride and a solvent, and then cooling and crystallizing to obtain the iron complex.The obtained iron complex can effectively induce cancer cell ferroptosis, can effectively inhibit the growth of breast cancer, lung cancer, liver cancer or glioma, has high selectivity, has small toxicity to normal cell strains, and has better treatment effect compared with existing clinical drugs, such as cisplatin.

The invention provides a gene marker combination and system for predicting glioma risk and application, and belongs to the technical field of glioma prediction. The gene marker combination comprises an NTN4 gene, an RBP1 gene, a TWIST1 gene, a GADD45G gene, an NUAK2 gene, a GRIK2 gene, a WEE1 gene and an RRM2 gene. Eight senescence-related genes are screened by screening senescence-related genes and combining survival data of glioma patients. The research finds that the risk scoring model constructed by using the eight senescence-related genes can specifically and sensitively predict the glioma risk, and has good prediction efficiency. According to the method, the glioma risk assessment model constructed on the basis of combining the risk scoring model with the clinical pathological variable factors is established, and compared with the capability of predicting the glioma risk of the risk scoring model, the glioma risk assessment model is better in prediction efficiency.

The invention discloses an MRI (Magnetic Resonance Imaging) image segmentation method based on symmetric multidirectional Mama and double attention, which comprises the following steps: firstly, establishing an image segmentation model based on a ResUNet backbone network, taking a standard image as input, mapping the standard image to a multi-scale semantic feature space through down-sampling, enhancing attention features of the image by adopting multi-scale self-adaptive attention, and obtaining an image segmentation result; and features are weighted and dynamically fused, collaborative optimization of channel-space dimensions and dynamic balance of local-global information are realized, multi-scale heterogeneity challenges of glioma are effectively handled, the perceptual performance of the network on tumor features of different scales is enhanced, four groups of symmetric scanning strategies are adopted to scan enhanced images, and the accuracy of the network is improved. The MRI image segmentation method comprises the following steps of: firstly, extracting a tumor image, enhancing feature expression of the image, improving the understanding ability of a model on a tumor global structure, and finally, sequentially adopting multi-operator edge detection, edge feature coding, adaptive intensity adjustment and gating residual fusion, fusing image features through symmetric up-sampling, outputting a segmented image, completing MRI image segmentation, and improving segmentation precision.

The invention belongs to the field of pathologies affecting the central nervous system: in particular, severe cerebral pathologies, more particularly those restricted by the presence of the blood-brain barrier (BBB): gliomas, cerebral metastases, neurodegenerative diseases (e.g. Alzheimer's, Parkinson's or ALS), genetic diseases (Huntington's, myopathies, Leigh syndrome, Rett syndrome), but also to the field of cancers, musculoskeletal and immunological disorders, vascular diseases (thrombus) in numerous organs (e.g. liver, kidney or muscle) and in combination with numerous therapeutic approaches (e.g. chemotherapy, immunotherapy, targeted therapy or gene therapy). The invention relates to a microbubble comprising a fluorinated polymer or copolymer and a fluorinated gas, to the use thereof and also to the polymer or copolymer intermediate compounds.

The invention belongs to the technical field of medical image processing and artificial intelligence, and particularly relates to a weakly supervised brain tumor segmentation method which comprises the following steps: acquiring a low-level glioma segmentation data set of a cancer genome map, and randomly dividing the data set into a training set and a test set according to patient division; based on the image data in the data set, carrying out transfer learning by using a pre-trained ResNet50 as a backbone network; a global optimization mechanism of an SZIO algorithm is utilized to perform adaptive optimization and intelligent screening on features extracted based on a traditional class activation mapping technology, and fine fusion and semantic enhancement of multi-level features are realized; and performing fine adjustment on the ResNet50 backbone network at a preset initial learning rate. According to the method, the performance breakthrough of weakly supervised brain tumor segmentation can be realized, the practicability, robustness and clinical transformation potential are relatively high, and an efficient and reliable intelligent segmentation solution is provided for the field of medical image analysis.

The invention discloses a positioning and delimiting method and system for a cortex pathological focus based on an intracranial electroencephalogram signal. The method comprises the following steps: constructing an individualized three-dimensional navigation model; obtaining an original intracranial electroencephalogram signal; generating a plurality of representative segments according to the original intracranial electroencephalogram signals; analyzing the original intracranial electroencephalogram signal and the plurality of representative segments so as to obtain a network analysis core index; generating a fusion type three-dimensional brain model according to the network analysis core index and the individualized three-dimensional navigation model; generating a space-time three-dimensional electroencephalogram model and an electrophysiology functional area distribution diagram according to the fused three-dimensional brain model and the network analysis core indexes; and according to the space-time three-dimensional electroencephalogram model and the electrophysiology functional area distribution map, generating accurate electrophysiology positioning and delimiting information of the cortical pathological focus. According to the invention, accurate electrophysiological delimitation of intra-operative cortical pathological lesions (glioma, hemangioma, focal cortical dysplasia and the like) is realized.

The application discloses a glioma IDH mutation state noninvasive prediction system based on a machine learning algorithm, relates to the technical field of medical image processing, and comprises a data acquisition module, a data processing module, a feature extraction module, a single nuclear magnetic sequence image classifier training module, a multi-nuclear magnetic sequence image classifier training module, a model evaluation module and a prediction module; the system acts on conventional nuclear magnetic sequence image data and FW sequence image data, FA sequence image data and MD sequence image data obtained by processing DTI sequence image data, and combines a machine learning algorithm to construct a prediction model, so that the extraction and analysis of features are more reliable and accurate, the limitation of DWI sequence image data in analyzing an IDH mutation state is solved, and the accuracy of the established prediction model is ensured.

The invention discloses an application of Siglec-10 / G or a fusion protein containing a Siglec-10 / G protein structure in preparation of a medicine for treating glioma. The application finds that Siglec-10 / G is a new key myeloid immune checkpoint molecule in a glioma immunosuppression microenvironment, and reveals a specific molecular mechanism that Siglec-10 / G and related action signals (such as MUC1, CKB and GOT1) thereof influence glioma generation. The Rebinant Mouse Siglec-10 (C-Fc) provided by the invention can be combined with a ligand on the surface of a tumor cell, so that endogenous Siglec-10 / G signal transduction on MDM is competitively blocked, the anti-tumor immunity of a tumor-bearing mouse is improved, the growth of glioma is inhibited, the survival time of the tumor-bearing mouse is prolonged, and the Rebinant Mouse Siglec-10 (C-Fc) can be used for treating glioma.