50 results about "Lymphoid Tumor" patented technology

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 CAR-T treatment response intelligent prediction system based on medical images, and relates to the field of medical image analysis and tumor immunotherapy, and the system comprises a data integration module, a multi-modal feature extraction module, a prediction model construction module, a model optimization module, a clinical analysis module and an adverse reaction risk early warning module. The system integrates PET-CT, MRI, ultrasound and other multi-mode medical image data, and extracts the form, texture and metabolic characteristics of the tumor; constructing a treatment response prediction model by adopting a three-dimensional convolutional neural network; innovatively applying a differential geometry theory, constructing a medical image manifold structure and generating a tumor adversarial sample maintaining topological characteristics, and executing an adaptive optimization strategy by using a Riemannian metric space; accurate prediction of CAR-T treatment response of a lymphoma patient is realized, and adverse reaction risk early warning is carried out through brain and visceral organ image analysis.

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.

We have discovered that allogeneic HSC-engineered human iNKT (3rdHSC-iNKT) cells display potent anti-GvHD functions, by eliminating antigen-presenting myeloid cells in vitro and in xenograft models, without negatively impacting tumor eradication by allogeneic T cells in preclinical models of lymphoma and leukemia. The 3rdHSC-iNKT cells closely resembled the CD4−CD8− / + subsets of endogenous human iNKT cells in phenotype and functionality. Embodiments of the invention harness these discoveries in new methods and materials for alleviating graft versus host disease.

This disclosure relates to methods and compositions for treating cancer including advanced solid tumor, a metastatic solid tumor or lymphoma using an inducible IL-12 prodrug.

The present invention discloses a class of flavonoid derivatives, their preparation methods, and applications. The structural formula is shown in Formula I: wherein X is selected from CH and N, and R1 is selected from H. These compounds can significantly inhibit the activation of STAT3, a cell signaling pathway, at low doses. Experimental results show that these compounds can significantly inhibit the proliferation of various tumor cell lines, including lymphoma, multiple myeloma, cervical cancer, breast cancer, gastric cancer, lung cancer, and pancreatic cancer. They can specifically inhibit STAT3 signaling activation and the expression of the downstream target gene cyclinD1, indicating that these compounds have the potential to be developed as targeted anti-tumor drugs related to the STAT3 signaling pathway.

An oncolytic group B adenovirus suitable for use in the treatment of solid tumors, such as sarcoma, cancer and / or lymphoma, comprising a sequence of formula (I): 5 'ITR-B1-BA-B2-BX-BB-BY-B3-3' ITR (I) wherein: a first transgene encodes a polypeptide comprising a target sequence specific for a binding domain on a cell of a cell-based immunotherapy, a target sequence, such as a protein with a (exogenous) recombinant surface expression, such as a chimeric antigen receptor or NKG2D receptor, in particular wherein the target sequence specifically binds to the surface-expressed protein, more particularly the chimeric antigen receptor, on an immunotherapy cell; and the second transgene encodes a polypeptide comprising a molecule that promotes the cell-based immunotherapy to enter and transport within a tumor.

The application provides a novel DNA methylation marker TAGMe-5 for tumor identification and use thereof. The tumor marker TAGMe-5 of the application shows significant DNA methylation difference between cancer adjacent tissue and cancer tissue, and the difference has significant statistical significance, and the difference is shown in numerous tumors such as solid tumors and non-solid tumors. The solid tumors include lung cancer, liver cancer, prostate cancer, cervical cancer, endometrial cancer, urothelial carcinoma, biliary tract tumor, etc. The non-solid tumors include hematological tumors, lymphoma, etc. Therefore, the tumor marker of the application can be used for clinical screening of pan-cancer, molecular diagnosis, prognosis and efficacy evaluation.

The invention relates to the technical field of image data processing, in particular to a lymphoma-oriented fluorescence image and pathological section information fusion method. A first image and a second image of the lymphoma tissue sample are obtained, the first image is a bright field image, and the second image is a FISH image; in the first image, the difference degree between each cell and a normal cell and the difference degree between each tissue boundary and a normal tissue boundary are analyzed, and a cell anomaly index and a tissue anomaly index are obtained respectively; determining a slice anomaly index according to the cell anomaly index and the tissue anomaly index, and adjusting an initial block size based on the slice anomaly index to obtain a target block size; and taking the target block size as a block size, and fusing the first image and the second image by using a double-flow convolutional network to obtain a fused image. According to the invention, the image fusion effect of the FISH image and the bright field image of the lymphoma pathological section can be improved.

This invention relates to the field of image data processing technology, specifically to a method for information fusion between fluorescence images and pathological sections of lymphoma. The method involves acquiring a first image and a second image of a lymphoma tissue sample; the first image is a bright-field image, and the second image is a FISH image. In the first image, the degree of difference between each cell and normal cells, as well as the degree of difference between each tissue boundary and normal tissue boundaries, are analyzed to obtain cell abnormality indices and tissue abnormality indices, respectively. Based on the cell abnormality indices and tissue abnormality indices, a section abnormality index is determined, and the initial block size is adjusted based on the section abnormality index to obtain a target block size. Using the target block size as the block size, a two-stream convolutional network is used to fuse the first image and the second image to obtain a fused image. This invention improves the image fusion effect between FISH images and bright-field images of lymphoma pathological sections.

The invention relates to a triple-effect T cell adapter for targeting T cell lymphoma TRBC1. The invention discloses a TRBC1-targeted triple-effect T cell adapter which is composed of an Fc region, a hinge region, a first Fab arm and a second Fab arm, and a third targeting module is covalently connected to the C end of the Fc region to form a Y-shaped triple-head antibody. The first Fab arm and the second Fab arm are respectively combined with TRBC1 and CD3, and the third Fab arm is combined with CD28 or CD137, so that three functions of tumor recognition, T cell recruitment and co-stimulation amplification are integrated. The two polypeptide chains are heterodimerized through CH3 region button mutation, and correct assembly is ensured. The structure supports module interchange, programmable regulation and control of synergistic signals, significantly enhances TRBC1 positive T cell lymphoma killing, reduces toxicity to normal T cells, has high activity and a wide safety window, and is suitable for preparation of related drugs.

This disclosure relates to methods and compositions for treating cancer, including advanced solid tumors, metastatic solid tumors, or lymphoma, using an inducible IL-12 prodrug. This disclosure relates to compositions and methods for treating cancer using an inducible IL-12 prodrug. This inducible IL-12 prodrug contains attenuated IL-12 and has a longer half-life compared to naturally occurring IL-12. Optionally, IL-12 may be mutein. IL-12 mutein may be aglycosylated or partially aglycosylated.

The application provides a lymphoma image data generation method based on conditional constraint and feature matching of a generative adversarial network, and is used for data enhancement of lymphoma images. The method first combines a label constraint, builds a full connection layer and four deconvolution layers for a generative model of WGAN-GP to generate images, then builds three convolution layers and a full connection layer for a discriminant model of WGAN-GP to identify whether the images are from the real or generated, and finally combines a feature matching method to establish a loss function, generates images with specified labels through the adversarial learning of the generative model and the discriminant model, so as to achieve the purpose of data enhancement. A large number of clinical cases are needed for the acquisition of lymphoma images, however, the method can solve the problem that workers are difficult to acquire specified type lymphoma image data in the prior art, further improves the work efficiency of workers, thereby reduces the working time of workers and improves the authenticity of the generated images.

The invention relates to the technical field of molecular biology, in particular to a molecular marker combination and a kit for B lymphoma cell detection or enrichment or PVRL prediction, diagnosis or prognosis, and B lymphoma cells are tumor cells of primary vitreoretinal lymphoma. Specifically, the invention provides a molecular marker combination capable of being used for detecting tumor cells of primary vitreous retinal lymphoma, and the molecular marker combination comprises ZBTB7A and CDKN2A.

The invention discloses a composition for treating ABC type diffuse large B-cell lymphoma and application of the composition, and relates to the technical field of biological medicine. The composition comprises the chidamide and the hispidulin. According to the invention, a synergistic treatment system of the composition chidamide and hispidulin is constructed, and the problem of treatment of ABC type diffuse large B-cell lymphoma is accurately solved. According to the composition, through targeting double key targets of HDAC3 and PIM1, the synergistic interaction of epigenetic regulation and signal channel inhibition is realized, and the anti-tumor effect is remarkably improved. The invention successfully breaks through the bottleneck that the existing R-CHOP scheme is poor in curative effect and easy to relapse in the treatment of ABC subtype lymphoma, provides a brand new high-specificity treatment strategy for relapse or refractory ABC type diffuse large B-cell lymphoma, and has a wide clinical application prospect.

The invention relates to a multitask lymphoma segmentation method based on PET-CT image fusion. The multitask lymphoma segmentation method comprises the following steps: inputting and preprocessing data; constructing a multi-task collaborative learning framework comprising a classification and segmentation network structure module, a PET-CT multi-modal feature fusion module and a multi-stage joint attention mechanism module, and performing training; and inputting the preprocessed data into the trained multi-task collaborative learning framework to obtain a result. According to the method, the depth and precision of PET-CT image information fusion are improved, a multi-modal feature fusion strategy is adopted, PET and CT modal information is fused in the two levels of space and channel, the image expression ability is improved, and the model can better recognize tumor areas with fuzzy boundaries and complex structures.

The application discloses a compound capable of targeted degradation of MYC and application thereof, and belongs to the technical field of medicines. The compound can target degradation of MYC after entering a patient's body, thereby effectively inhibiting the proliferation of tumor cells such as lymphoma and osteosarcoma and cancer cells such as colorectal cancer, and having good anti-tumor effect and anticancer effect; the compound can also inhibit the proliferation of myelomonocytic leukemia cells in the patient's body, thereby providing a new treatment scheme for treating myelomonocytic leukemia; the compound has a synergistic effect when combined with doxorubicin, has better anti-tumor activity, and has a wide application prospect in the field of medicines.

Methods of treatment and / or prevention of tumours, preferably of solid tumours, more preferably of breast cancer, melanoma, bladder cancer, head and neck cancer, Hodgkin's lymphoma, kidney cancer, non-small cell lung cancer using fermented supernatant, or fractions thereof, of the Lactobacillus casei or paracasei species are disclosed. The species is the strain deposited according to the Budapest Treaty with No. CNCM I-5220 and / or includes in its DNA genome a DNA sequence essentially identical to one of: SEQ ID No 1 to 5.

This invention discloses the use of the phenylalanyl-tRNA synthetase α subunit in the treatment of non-Hodgkin's lymphoma. Experiments have demonstrated that reducing or increasing the expression level of FARSA in non-Hodgkin's lymphoma cell lines can effectively regulate the cell cycle and proliferation of tumor cells. This invention provides a new target for the treatment of non-Hodgkin's lymphoma.

The invention discloses an interferon regulatory factor 4 (IRF4) inhibitor as shown in a formula (1) and a preparation method thereof, and a target product is prepared by taking dinorhydrin as a raw material through reactions such as TBS protection, 4, 4-dimethylation, TBS protection removal, oxidation, esterification, reduction, iodination, cyano substitution, Wittig, hydrolysis, esterification, amidation, condensation and the like. The invention further discloses an application of the IRF4 inhibitor in the aspect of treating multiple myeloma (MM), and further discloses an application of the IRF4 inhibitor in the aspect of treating multiple myeloma (MM). The compound can selectively inhibit the multiplication capacity of IRF4 high-expression myeloma cells, and has a good application prospect in treatment of IRF4 high-expression tumors such as multiple myeloma and lymphoma.

The present invention relates to a thyroid lymphoma prediction method and device based on machine learning, which belongs to the field of image processing technology and solves the problem of the lack of thyroid lymphoma prediction methods based on deep learning in the prior art. The specific steps include: annotating the thyroid ultrasound image with the lesion area segmented, including the lesion type label of lymphoma, to obtain the original data set; extracting the tumor imaging features of the original data set to obtain the initial features, and performing zero importance analysis and correlation screening in sequence to obtain the final screening features with high importance and low correlation; based on the final screening features and the annotated lesion type labels, using machine learning training to obtain a thyroid lymphoma prediction model; performing feature extraction on the thyroid ultrasound segmentation image to be tested with the suspected lesion area segmented, to obtain the corresponding final screening features; based on the corresponding final screening features, using the thyroid lymphoma prediction model to achieve accurate prediction of thyroid lymphoma.

The application relates to a kind of automatic classification methods of children lymphoma type based on imageomics, belong to artificial intelligence technical field.It includes the following steps: collecting children lymphoma CT image, constructs data set;The original three-dimensional CT image is preprocessed and adaptively gray window is adjusted, and preprocessed three-dimensional CT image data is obtained;Imageomics feature extraction and region perception gradient weighted screening are carried out, and the comprehensive screening score of each feature is obtained;A classification model of children lymphoma type is constructed, the enhanced imageomics feature, the clinical prior feature and the deep feature modulated by region perception weight are fused, the adaptive fusion of features is realized by using dynamic gating mechanism, and the category probability vector is output;The model is trained and optimized by a composite loss function, and a trained model is obtained;The CT image of children lymphoma to be classified is input into the trained model, and a classification result is obtained.The application can improve the accuracy of automatic classification of children lymphoma type.

The invention relates to the field of biological hospitals, and discloses a tumor follicular helper T cell strain for vascular immune mother cell T cell lymphoma as well as a construction method and application of the tumor follicular helper T cell strain. The invention discloses an AITL tumor TFH cell strain, the name of which is human tumor follicular helper T cell AITL-CH1. The strain is preserved in CCTCC; the preservation date is 2025 to 08 to 12; the preservation number is CCTCC (China Center For Type Culture Collection) NO: C2025233 The construction method comprises the steps of isolated culture of primary cells, in-vitro induction culture of TFH cells and isolated passage. The tumor TFH cell strain of the AITL patient is successfully established, the cell strain has stable TFH characteristic phenotype and good multiplication capacity, the limitation of short in-vitro culture period of primary cells is effectively overcome, and a foundation is laid for in-depth study on AITL epigenetic modification and molecular pathogenic mechanism. A reliable and stable cell model is provided for clinical immunotherapy and targeted drug research and development.

The invention discloses an innovative nnFormer enabled ultra-precise positioning system for a lymphoma focus. The innovative nnFormer enabled ultra-precise positioning system for the lymphoma focus is provided. Comprising a user layer which interacts with an external user and is used for acquiring image data from the external user or feeding back a positioning result to the external user; the data management layer communicates with the user layer and is used for receiving the acquired image data and performing storage management on the image data; the AI intelligent processing module is in communication connection with the data management layer; the front-end medical view module communicates with the user layer, and the front-end medical view module comprises a view basic tool, a window level control module, a multi-section control module and a color control module; and iteratively updating the nnFormer model, the tumor focus positioning algorithm and the AI auxiliary diagnosis logic in an automatic or semi-automatic manner. In addition, the module also adopts advanced technologies such as reinforcement learning and the like, so that the system can continuously optimize the performance in actual use, and the accuracy and generalization ability are improved.

The application belongs to the technical field of medical image processing and computer-aided diagnosis, and discloses an orbital lymphoma and inflammatory pseudotumor classification method, system, device and terminal, in the obtained orbital DCE-MRI image, the tumor region and the eye cone triangular region in the original image are manually segmented and pretreated; the neural network is used for extracting the features of the tumor region, and the features are subjected to clustering statistical analysis as the tumor region features; the eye cone triangular region is subjected to feature extraction as a similarity judgment standard, and the self-classification and self-recovery network is used for extracting the eye cone region features; the original image is used as the input network and combined with the features of the tumor region and the eye cone triangular region to train the network model; the multi-modal orbital data is input into the classification model for processing, so that the orbital lymphoma and inflammatory pseudotumor are classified. The application comprehensively considers the texture features of the tumor and the depth features of the eye cone region, effectively improves the prediction accuracy, and has the characteristics of high accuracy.

The present invention relates to a new vilazodone derivative (3-(4-(4-(2-chloro-6-methoxyphenyl)piperazine-1-yl)butyl)-cyano-indole) and a synthesis method and application thereof. The vilazodone derivative is 3-(4-(4-(2-chloro-6-methoxyphenyl)piperazine-1-yl)butyl)-cyano-indole, and its chemical structure is as follows: The compound has a significant growth inhibitory effect on a variety of human tumor cells and has excellent inhibitory activity on tumors such as leukemia, lymphoma, breast cancer, melanoma, ovarian cancer, colorectal cancer, cervical cancer, lung cancer, prostate cancer, esophageal cancer, glioma, kidney cancer, pancreatic cancer, nasopharyngeal cancer, liver cancer, and gastric cancer. The IC50 effective concentration of the compound on tumor cells such as breast cancer, colorectal cancer, lung cancer, glioma, pancreatic cancer, and nasopharyngeal cancer is lower than that of cisplatin. Experiments show that the vilazodone derivative has a broad-spectrum anti-tumor activity and is expected to become a new anti-tumor drug.

The application relates to the fields of biotechnology and targeted therapy, and particularly relates to a recombinant construct, a CAR-T cell and construction and application thereof. The application verifies the role and mechanism of THEMIS in maintaining the long-term killing of tumor cells of CAR-T through experiments. The CAR-T cell overexpressing THEMIS is constructed, compared with a control group, the proliferation capacity and memory cell formation capacity of the THEMIS-7x19 CAR-T cell are enhanced after antigen stimulation, and the cytokines are in a controllable state, the cell apoptosis and exhaustion are reduced, and the long-term tumor killing capacity can be maintained more effectively. The in-vivo experiment also proves that the THEMIS-7x19 CAR-T cell has stronger anti-tumor effect compared with the control group in a lymphoma tumor-bearing mouse model, and the survival time of the mouse is significantly prolonged.

The invention relates to the technical field of image processing, in particular to an intelligent processing method of a fluorescence in-situ hybridization image, which comprises the following steps: screening fluorescence connected domains based on the color distribution condition of the fluorescence connected domains to obtain an overlapping fusion region; screening all the cell connected domains through the overall characteristic value to obtain a translocation cell connected domain and an overlapping cell connected domain; according to the edge smoothness and the distribution condition of the fluorescent connected domains in the overlapped cell connected domains, obtaining the fluorescent region distribution regularity of the overlapped cell connected domains; distinguishing all overlapped cell connected domains according to fluorescent region distribution regularity to obtain proliferative cell connected domains; the lymphoma is intelligently classified based on the number of proliferative cell connected domains and translocation cell connected domains. According to the invention, the accuracy of tumor type classification of lymphoma cells is improved.

The application belongs to the field of intelligent medical treatment, and particularly relates to an artificial intelligence feature extraction method and system based on lymphoma MICM phenotyping. The method comprises the following steps: S1, obtaining a diagnosis report of a to-be-tested person; S2, obtaining a structured diagnosis report based on the diagnosis report; S3, vectorizing the structured diagnosis report to obtain a vectorized diagnosis report; S4, inputting the vectorized diagnosis report into a quantum coding-based Transformer for feature extraction to obtain MICM phenotyping features; the quantum coding-based Transformer converts input data into a quantum state and extracts quantum state feature information, and converts the quantum state feature information into non-quantum state data and outputs the MICM phenotyping features. The application extracts the required MICM features by using the quantum coding-based Transformer, and enhances the data feature capturing capacity of the model by using the quantum coding-based Transformer algorithm.

The objective of the present invention is to treat cancer in affected dogs by using interleukin-15, which plays a pivotal role in both innate immunity and acquired immunity. Specifically, the present invention relates to a composition for treating cancer in mammary tumor- or lymphoma-affected dogs, or an anticancer immune enhancer, comprising recombinant canine interleukin-15. The present invention also relates to the dosage and use of recombinant canine interleukin-15 in the treatment of cancer in mammary tumor- or lymphoma-affected dogs. The present invention can exhibit therapeutically effective anticancer- or anticancer immunity-enhancing efficacy by minimizing harmful effects or side effects after standard treatment in mammary tumor- or lymphoma-affected dogs by using recombinant canine interleukin-15.