154 results about "Tumor type" patented technology

The invention relates to the technical field of big data, in particular to a cloud computing-based tumor early screening data sharing platform construction method and system. The sharing platform construction method comprises the following steps: carrying out association modeling on multi-dimensional early screening data by adopting a knowledge graph technology; encrypting the sensitive field of the metadata index database, and synchronously generating an irreversible anonymous identifier to replace the original identifier; partitioning and arranging the metadata index database in a distributed storage system corresponding to a cloud platform according to tumor types, and performing cross-region redundant backup by adopting an erasure code technology; and constructing an access control smart contract based on a block chain technology, verifying the qualification of a requester through a digital identity authentication module, and after verifying the validity of a digital certificate of the requester, dynamically decrypting and transmitting a data copy conforming to an authority level. Through organic fusion of the knowledge graph, the encryption technology, distributed storage and the block chain, a safe, efficient and reliable tumor early screening data sharing platform is constructed.

The invention provides a tumor type bone defect reconstruction method and system. The method comprises the following steps: acquiring bone defect information; constructing a bone defect restoration; constructing a bone defect three-dimensional model; and bone defect simulation surgery. According to the method, the bone defect information is obtained by evaluating the illness state of the patient, meanwhile, the anatomical structure of the bone tumor of the patient is subjected to structural evaluation through modern medical imaging, and the bone defect restoration is constructed in combination with the corresponding bone defect information; then, according to preset prosthesis design data, carrying out repair design on the bone defect prosthesis and obtaining three-dimensional skeleton data of a patient in real time, then, according to the obtained three-dimensional skeleton data, constructing a three-dimensional bone defect model and carrying out a bone defect simulation operation, and finally, predicting and optimizing the implantation position, direction and angle of the bone defect prosthesis. The effect of improving the construction accuracy of the bone defect prosthesis is achieved, and the problem that in the prior art, the construction accuracy of the bone defect prosthesis is low is solved.

The invention discloses an intelligent processing method and system for multi-modal biological medicine data. The method comprises the following steps: acquiring a tumor tissue microscopic image and clinical characteristics of a diagnosed patient object; performing semantic coding on the clinical features to obtain clinical feature semantic understanding feature vectors; performing feature extraction on the tumor tissue microscopic image through a tumor tissue morphological feature extractor based on a deep neural network model to obtain a tumor tissue microscopic feature map; performing target significance analysis on the tumor tissue microscopic feature map to obtain a sequence of target manifestation tumor tissue microscopic local feature vectors; performing class attention interaction fusion processing on the sequence of the clinical feature semantic understanding feature vector and the target explicit tumor tissue microscopic local feature vector to obtain a multi-modal medical semantic fusion feature; and determining a tumor type label based on the multi-modal medical semantic fusion features. Therefore, the tumor type can be detected and judged, and powerful support is provided for the aspects of early diagnosis, personalized treatment and the like of tumors.

The invention discloses a tumor whole course intelligent management platform and method, and particularly relates to the technical field of medical model management. Carrying out semantic analysis and uniform format processing on the multi-modal data from different sources by utilizing a natural language processing technology and a standardized tool; using a feature selection algorithm to screen key features related to tumor types and treatment schemes; through analyzing mutual information of the key features and the target variables and mutual information among the key features, evaluating the degree of redundant hidden dangers existing in the screened key features; the semantic consistency of the multi-modal data from different sources is evaluated by comparing and analyzing the prediction label and the actual label of the multi-modal data; and comprehensively analyzing the redundant hidden danger degree of the screened key features and the semantic consistency of the multi-modal data from different sources, and judging whether the personalized treatment effect prediction model can be constructed or not.

A three-dimensional scaffold for use in engineered tissue includes a repeated 3D geometry; and at least one of a hydrogel, a biocompatible polymer, and a bioprintable polymer. The repeated 3D geometry may include an open geometry including a plurality of interconnected nodes configured to be seeded with living cells. The scaffold may be formed via bioprinting. The three-dimensional scaffold may also be used for (1) 3D tissue modelling (for liver, kidney, gut, pancreas, heart, lung, spleen, etc.), (2) disease modeling of different tumor types such as breast, colorectal, lung, liver, brain, ovarian and others, (3) drug testing / therapeutic screening of small molecule drugs, peptides, proteins, antibodies, ADCs, gene therapies, RNA-based therapeutics, cell therapies, and tissue therapies, and (4) therapeutic / drug delivery (i.e., via delivery device that can be used to control drug release based on different patterns).

This disclosure describes a Machine Learning (ML) framework, including graph convolution neural networks (GCN) for identifying gene expression signatures related to cancer driver events. The model is trained to identify the TP53 mutational state of cancer samples from gene expression, utilizing comprehensive curated graph structures of gene interactions. A quantitative score is generated to rank the severity of a driver event in each sample. Very high AUG results on unseen data across several tumor types are achieved with this method. There is a strong correlation with protein function. The Signatures in Transcriptome Associated with Mutated Protein (STAMP) model can also predict driver events in many combinations of important cancer genes / pathways and several tumor types, based on well-established annotations from the literature. The STAMP model therefore can identify and quantify driver events, which may lead the way to improved targeted therapy selection and prioritization in cancer patients.

The invention relates to the technical field of medical information systems, in particular to a tumor follow-up visit management method and system based on multi-source data fusion, and the method comprises the steps: obtaining patient data from an electronic medical record, an examination report and a follow-up visit record; based on the patient data, adopting a natural language processing technology to extract structured follow-up visit knowledge; according to follow-up visit knowledge, constructing a'patient-main diagnosis doctor-tumor type 'triple (P, C, c); based on the triple (P, C, c), mapping with a hospital disease score library, a doctor score library, a hospital grade corresponding database and a doctor individualized database by using a rule engine to obtain a patient score P and a doctor score Rc; a personalized follow-up scheme and a patient compliance evaluation result are generated, and comprehensive and accurate patient information is provided for doctors through multi-source data fusion. Therefore, the accuracy of follow-up decision making is improved, the time for doctors to collect information is greatly shortened, and the working efficiency is improved.

The invention provides a novel platform for tumor immunotherapy. The platform is formed by self-assembly of full-heavy-chain human ferritin which can be jointly fused and expressed by a polypeptide prodrug mask which can be activated by protease and a mitochondrial disrupting peptide, and can specifically target a tumor microenvironment and induce ICD death of tumors. Meanwhile, an ion channel is formed in a protein shell of the platform, so that immunocompetence metal ions can be loaded in a cavity of the platform, and time-space synchronous anti-tumor immune activation can be carried out in cooperation with ICD. As the mitochondrial disrupting peptide and the metal ions can be optimized according to tumor types, the platform can be used for treating various tumors.

In some embodiments, a set of biomarkers is provided, selected from one or more of the group consisting of ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP, and RNF213. The biomarker is used for predicting the response of a subject receiving immunotherapy to immunotherapy, has good performance in subjects suffering from different tumor types, has high universality, has the advantages of being low in cost, easy and convenient to use and the like, and has good application prospects. And the method is of great significance for predicting response to immunotherapy of cancers and personalized therapy.

The invention relates to the technical field of tumor data processing, in particular to a methylation-based tumor data processing system. The system comprises the following modules: a methylated sample sequencing module, a sequencing difference site recognition module, a tumor gene sequence analysis module and a tumor subtype classification module, performing high-throughput sequencing on the to-be-detected clinical tumor DNA sample to obtain methylated tumor sequencing data; performing methylation level quantification on the methylated tumor sequencing data, and performing tumor difference site analysis to generate tumor difference site data; performing tumor generation key gene sequence identification according to the tumor difference site data to generate tumor methylation characteristic data; and performing tumor subtype tag identification according to the tumor methylation characteristic data to generate tumor type tag data. According to the method, accurate tumor typing is realized on the basis of tumor DNA methylation characteristic analysis, and the false negative rate of low-concentration sample detection is effectively reduced.

The invention discloses a novel polypeptide, a polypeptide derivative and application thereof, and belongs to the technical field of biological medicine. The invention specifically relates to a specific sequence, a discovery process, a specific anti-tumor type, a long-acting modification mode and application of the novel polypeptide. The tumors comprise one or more of brain glioma, neuroblastoma, head and neck cancer, esophagus cancer, thyroid cancer, lung cancer, liver cancer, kidney cancer, breast cancer, cervical cancer, uterine cancer, ovarian cancer, colon cancer, small intestinal cancer, ileocecum cancer, stomach cancer, bladder cancer, pancreatic cancer, prostate cancer, bile duct cancer, melanoma, sarcoma, myeloma, lymphoma and leukemia; the compound has the specific effect of inhibiting proliferation and / or metastasis of tumor cells, is wide in treatment spectrum and has important treatment value in various tumors.

The invention relates to the technical field of intelligent control systems, in particular to an intelligent control system of a tumor electric field treatment device, which comprises a resistivity acquisition module for measuring tumor electrical impedance under differentiated frequencies by using a four-electrode measuring instrument, recording resistivity data changing along with time, enhancing the signal-to-noise ratio of signals by using a differential amplification technology, and acquiring the tumor electrical impedance by using the resistivity acquisition module; and calibrating the reading of the instrument, and generating a calibrated resistivity parameter set. According to the method, by analyzing the correlation between tumor electrical impedance and gene expression data, accurate adjustment of treatment electric field parameters is achieved, the treatment pertinence and the treatment effect are improved, the method allows personalized adjustment of a treatment scheme according to specific biomarkers of tumors, optimal matching of the electric field parameters and specific tumor characteristics of patients is ensured, and the treatment effect is improved. The application of clustering analysis realizes fine group division in patients with different tumor types or stages, so that the electric field treatment can be optimized according to specific tumor biological behaviors, and the phenomenon of one-time cutting possibly occurring in traditional treatment is avoided.

The invention discloses a method and a system for constructing an intestinal obstruction pathogenesis identification model based on multi-phase feature fusion, which are suitable for clinical diagnosis scenes with complex intestinal obstruction types and various image expressions. The method comprises the following steps: acquiring a multi-stage CT image of a patient, performing image preprocessing and structural partitioning, extracting phase-to-phase structural consistency, identifying a pseudo-difference area and a non-focus interference area, and suppressing the characteristics of the pseudo-difference area and the non-focus interference area; and meanwhile, in combination with disease cause region difference enhancement, structure expectation template comparison and dynamic tolerance analysis, intelligent discrimination of multiple disease causes such as adhesion type, tumor type, telescoping type and the like is realized. The system has the advantages of being high in model interpretability, sensitive to negative signs and high in artifact interference resistance, and the accuracy and clinical applicability of automatic identification of ileus causes are improved.

The invention discloses a tumor patient test laboratory sheet interpretation system and method, and relates to the field of medical informatization, and the system comprises an information verification module which is used for the identity verification and classification of a patient and a doctor; the laboratory test report analysis module is used for carrying out structured analysis on the test laboratory test report through natural language processing and a computer vision technology, and verifying the qualification of a detection unit based on a fuzzy matching algorithm; the data storage module is used for integrating knowledge base data related to tumor diagnosis and treatment; and the index analysis module is used for performing tumor type identification, index abnormal item analysis and triage matching through a cascade model of a support vector machine, an isolated forest and a decision tree. According to the technical scheme, automatic interpretation of the test laboratory sheet of the tumor patient and matching of professional doctors can be achieved, and the method has high application value.

The invention relates to the field of medical resource scheduling, in particular to a multi-chamber particle therapy method, system and device, a storage medium and a computer storage medium, and the system comprises an irradiation source which is used for generating a particle beam; the plurality of particle treatment rooms are used for sending beam requests and receiving the particle beams; the beam transport device is used for transmitting the particle beam from the irradiation source to the particle treatment room; and the control device is used for receiving the beam requests of the particle treatment room, calculating the priority for each beam request according to the reserved irradiation time, the tumor type and the patient condition included in each beam request, and controlling the beam transport device to transport the particle beams according to the priorities. According to the method, the utilization efficiency of beam resources can be remarkably improved, the urgency degree and clinical importance of treatment are fully considered, and the demand intensity of different requests for the beam resources can be quantitatively reflected.

The invention relates to the field of medical artificial intelligence, in particular to a mucus-like brain tumor multi-source data auxiliary differential diagnosis system based on deep learning, and the system collects pathological images, medical images, immunohistochemical data and molecular biology data through a multi-source heterogeneous data collection and standardization module, and carries out standardization processing; a multi-scale feature extraction module extracts key features from the data to generate multi-modal feature data; a cross-modal correlation analysis module analyzes correlation among the feature data and generates attention mapping data and feature correlation data; the diagnosis reasoning and decision-making module identifies the tumor type by using the data, carries out differential diagnosis and calculates the uncertainty of a diagnosis result; the interpretability display module is used for generating a structured diagnosis report which comprises multi-level attention visualization data and positioning a key diagnosis area; and through multi-source data fusion and correlation analysis, the diagnosis accuracy is remarkably improved.

The invention relates to a sensor for traceless detection of colorectal adenocarcinoma and colorectal cancer types and a manufacturing method and a detection method thereof. The sensor is a terahertz biomedical sensor composed of a substrate and an electromagnetic resonance unit array arranged on one side of the substrate. The electromagnetic resonance unit array adopts a periodic non-contact double-gap split resonator design, and can effectively adjust the transmission characteristics of terahertz waves. Different types of cells have respective unique dielectric constants, and the dielectric constants are associated with harmonic peak offset of a terahertz wave transmission spectrum and can be used as a basis for detecting and distinguishing tumor types. By means of the method, changes of physical characteristics of colorectal cancer cells of different types are quantified, and intelligent judgment of tumor types is achieved. The terahertz biomedical sensor has the advantages of rapidness and high sensitivity, can significantly improve the early diagnosis and precise classification capabilities of colorectal cancer, and provides an efficient detection tool for clinical application.

The present disclosure relates to methods for characterizing cancer testis antigen burden (“CTAB”), for predicting cancer survival outcomes using CTAB analysis, and for recommending and / or treating cancer using CTAB analysis. Particularly, aspects are directed to measuring expression of a panel of cancer testis antigen (CTA) gene markers in a tumor sample from a subject, determining a CTAB based on the measured expression of the CTA gene markers, classifying, by comparing to a reference CTAB score from the same cancer or tumor type, the CTAB score as a high-CTAB score or a low-CTAB score, and identifying the subject as responsive to a treatment wherein (i) the high-CTAB score is indicative of responsiveness to the treatment, or (ii) the low-CTAB score is indicative of responsiveness to the treatment.

Pancreatic adenocarcinoma has the worst overall mortality of any solid tumor, with only 7% of patients surviving after 5 years. To evaluate the clinical implications of genomic alterations in this low cellularity tumor type, we deeply sequenced the genomes of 101 enriched pancreatic adenocarcinomas from patients who underwent potentially curative resections and used non-invasive approaches to examine tumor specific mutations in the circulation of these patients. These analyses revealed somatic mutations in chromatin regulating genes including MLL and ARID1A in 20% of patients that were associated with improved survival. Liquid biopsy analyses of cell free plasma DNA revealed that 43% of patients with localized disease had detectable circulating tumor DNA (ctDNA) in their blood at the time of diagnosis. Detection of ctDNA after resection predicted clinical relapse and poor outcome, and disease recurrence by ctDNA was detected 6.5 months earlier than with standard CT imaging.

The present invention provides the use of an agent targeting the interaction between STC2 and Cav1.2 in the preparation of a drug for tumor prevention, treatment, or drug resistance enhancement. The agent targeting the interaction between STC2 and Cav1.2 is selected from an agent that inhibits STC2 expression, an agent that promotes Cav1.2 expression, or a combination of the two. The interaction between STC2 and Cav1.2 discovered in the present invention can be used for targeted therapy of ovarian cancer or other tumor types.

A system for generating a cancer alleviation nourishment plan including a computing device configured to receive at least a cancer biomarker relating to a user, where the cancer biomarker indicates the presence of cancer, retrieve a cancer profile related to the user, assign the cancer profile to a cancer category, wherein the cancer category includes a determination of a type of tumor, identify, using the cancer profile, a plurality of nutrition elements for the user, identifying, as a function of the plurality of nutrient amounts, the plurality of nutrition elements for cancer alleviation, and generate, using the plurality of nutrition elements, a cancer alleviation nourishment plan. A method for generating a cancer alleviation nourishment plan is also disclosed.

Provided is a methylated tumor marker STAMP-EP2 and uses thereof in preparing a tumor diagnostic reagen. The tumor marker STAMP-EP2 herein is hypermethylated in all tumor types, is hypomethylated in corresponding normal tissue, and has high sensitivity and specificity; primers for detecting STAMP-EP2 may be used to prepare a tumor diagnostic kit.

A method for controlling ultrasonic waves to induced sonoporation of a drug into cancer cells in a tumor. The method may include accessing configuration data that may include a type of the tumor and a type of drug, and determining values of operational parameters that may be based on the configuration data to define determined values. The operational parameters may include frequency value and duty cycle value. The method may also include determining frequency value which may be based on the type of tumor that may define a determined frequency value, and determining the duty cycle value which may be based on the type of drug and the type of tumor that may define a determined duty cycle value.

The invention provides a polyporus polysaccharide microneedle composition with a CD47 protein degradation function. The polyporus polysaccharide microneedle composition comprises a lysosome targeting chimera for degrading CD47 protein, polyporus polysaccharide and hyaluronic acid. The invention also provides a preparation method of the lysosome targeting chimera for degrading the CD47 protein. The invention also provides a microneedle prepared from the polyporus polysaccharide microneedle composition. The invention also provides a preparation method of the polyporus polysaccharide microneedle with the CD47 protein degradation function and application of the polyporus polysaccharide microneedle in preparation of medicines for treating tumors. According to the microneedle, on one hand, CD47 protein in melanoma tumor tissue can be efficiently degraded, then the phagocytosis effect of macrophages on tumor cells is enhanced, on the other hand, polyporus polysaccharide in the microneedle can promote the macrophages to be polarized towards anti-tumor types, and the anti-tumor activity of the macrophages is further enhanced. The immunosuppressive microenvironment is remodeled while CD47 is degraded, and the efficient and synergistic anti-tumor effect of the two drugs is achieved.

This platform is a privacy-preserving, federated learning system designed to integrate whole-slide digital pathology images with matched molecular profiling and clinical metadata for improved diagnosis, subtyping, and prognostic estimation of gynecologic tumors. The architecture comprises local institutional nodes that retain raw patient data while participating in distributed model training coordinated by a central orchestration server. Each local node preprocesses whole-slide images into patch-level tensors, extracts visual embeddings via convolutional backbones, and processes molecular vectors (e.g., somatic mutations, expression summaries, copy-number measures) via a molecular encoder. A multimodal fusion module - implemented as an attention- based transformer - integrates image and molecular embeddings into a unified representation used by multi-task heads for classification (histologic subtype, diagnostic label) and regression (risk score). The federated learning controller aggregates encrypted model updates (FedAvg) and returns improved global weights without exchanging raw data, enabling cross-site generalization while preserving patient privacy. Explainability components generate attention maps and tile-level saliency (Grad-CAM style) linked to molecular features, providing interpretable morpho- molecular correlations to pathologists. The platform supports API integration with PACS / LIMS, conforms to privacy standards via optional differential privacy and secure aggregation layers, and is extensible to additional omics modalities or transfer / fine-tuning workflows for related tumor types. By combining multimodal fusion, federated training, and clinician-facing interpretability, the system accelerates robust, generalizable AI for precision pathology in gynecologic oncology.

The invention belongs to the technical field of tumor immunity, and particularly relates to a tumor antigen and chemotactic factor co-coding system and application thereof. The invention provides a tumor antigen and chemotactic factor co-coding system. The tumor antigen and chemotactic factor co-coding system comprises a nucleotide sequence for coding a tumor antigen and a nucleotide sequence for coding a chemotactic factor. The tumor antigen and chemotactic factor co-coding system can flexibly replace an antigen sequence and chemotactic factor combination, is adaptive to different tumor types and various immunotherapy requirements, can be expanded to various solid tumor treatment scenes through a customized antigen-chemotactic factor combination in the future, and has a wide application prospect. The broad-spectrum application in tumor treatment means including tumor neoantigen vaccines, adoptive cell therapy and the like is realized.

To provide a method for quantifying molecular activity in cancer cells of human tumors, which predicts expression profiles of cancerous cells and non-cancerous cells respectively based on multiple sets of expression profiles.SOLUTION: A method includes the steps of: obtaining each set of multiple sets of expression profiles from tumour-derived samples including a mixture of cancerous cells and non-cancerous cells of one tumour type and determining tumour purity values for the tumour-derived samples; providing the multiple sets of expression profiles including combined expression data for multiple or all molecules expressed by cancerous cells and non-cancerous cells included in the tumour-derived samples; and deconvoluting each combined expression data by extrapolating expression profiles to a tumour purity value at least substantially equal to 1 or 0, thereby predicting the expression profiles of the cancerous cells and the non-cancerous cells respectively.SELECTED DRAWING: Figure 3

The present invention relates to the field of pathology analysis technology, and more specifically to a tumor pathology analysis method based on multimodal artificial intelligence, comprising the following steps: acquiring multi-center, multi-type whole-slice tissue images, cutting the whole-slice images into pathology images, and dividing the standardized pre-processed pathology images into training and test sets; constructing a contrastive language and image model for capturing key features in pathology images through a two-stage pre-training process; and fine-tuning the model using the pre-trained weights of the contrastive language and image model as initial weights and adding task-specific fully connected layers to obtain a pathology analysis model for implementing different classification and regression tasks in tumor pathology analysis. By integrating multimodal information from images and text, the present invention can more comprehensively assess tumor type, stage, and prognosis, thereby fully leveraging the advantages of multi-source data and effectively addressing the current bottlenecks in tumor pathology analysis technology.

The invention discloses a tumor tissue segmentation method based on an MR image, and relates to the technical field of image processing, and the method comprises the following steps: processing before segmentation, obtaining the MR image, quantifying texture features, calculating different-dimension feature values of each pixel of the MR image, obtaining a multi-scale texture feature vector of each pixel, and classifying the texture features. The method comprises the steps of dividing a pixel category and an image area of each pixel, enhancing boundary dynamic features and adjusting a threshold value, and has the advantages that double-weight boundary filtering is carried out by adopting confidence and spatial distance, similar texture features in a boundary transition area are amplified, boundary over-segmentation and under-segmentation probabilities are reduced, the judgment accuracy of the pixels in the boundary transition area is improved, and the judgment accuracy of the pixels in the boundary transition area is improved. According to the method, the threshold value of each image can be dynamically adjusted according to the tumor features of the image, manual parameter adjustment is not needed, multi-scene adaptation is achieved, the efficiency is improved, and through combination of multi-scale textures and dynamic double-feature fusion, the segmentation precision of cross-tumor types is guaranteed, and the heterogeneity tumor adaptability is improved.

The invention belongs to the field of medicinal chemistry, and particularly relates to a targeted drug coordinated metal complex as well as a preparation method and application thereof. The metal complex prepared by the invention is a ruthenium (II), iridium (III) and rhodium (III) complex coordinated with a targeted drug, the original biological activity of the ligand is remarkably optimized and improved by virtue of the synergistic effect between central metal ions and the ligand, the specific binding capacity of the ligand with a tumor cell target spot is enhanced, and the tumor cell target spot is further enhanced. The metal complex has a broad-spectrum and efficient anti-tumor activity, the action range of the metal complex can cover multiple common tumor types including but not limited to liver cancer, kidney cancer, breast cancer, ovarian cancer or colorectal cancer and the like, and an important technical support is provided for developing novel anti-tumor drugs with multiple target points and low toxic and side effects. The complex provided by the invention is novel in structure and good in anti-tumor activity, and has a good application prospect in the field of anti-tumor drugs.