1124 results about "Breast cancer" patented technology

The invention relates to a breast cancer plasma transporter miRNA marker and application thereof, and belongs to the technical field of biological detection. In the prior art, biomarkers for breast cancer liquid biopsy have the problems of low content, high heterogeneity, insufficient time resolution and the like, so that the detection reliability is limited. According to the invention, a specific miRNA (micro Ribonucleic Acid) marker (including one or more of let-7d-5p, miR-103a-3p and miR-22-3p) of the plasma transporter is provided. The marker can be used for preparing a breast cancer detection reagent or kit. Clinical verifications show that the miRNAs are remarkably and highly expressed in plasma migrators of breast cancer patients, the AUC value reaches 0.763-0.808, the sensitivity is 66.7%-86.7%, the specificity is 66.7%-88.3%, the miRNAs have the advantages of high signal-to-noise ratio and dynamic monitoring, and the accuracy and practicability of breast cancer detection are remarkably improved.

The invention discloses a breast cancer lymph node metastasis prediction system based on a gene spectrum, and relates to the technical field of breast cancer prediction systems. Comprising a data acquisition module which collects gene spectrum data of a breast cancer patient and collects detailed clinical information of the patient; the preprocessing module is used for carrying out data cleaning and data normalization processing on the collected data; and the feature extraction module is used for extracting principal component features by applying principal component analysis on the basis of the gene expression data. According to the method, gene expression data are considered, various gene spectrum data such as gene mutation and copy number variation and detailed clinical information are integrated, the biological characteristics of the breast cancer can be reflected more comprehensively, and the prediction accuracy is improved.

The invention relates to a breast cancer cardiotoxicity risk prediction system based on multi-modal data fusion. According to the system, multi-source heterogeneous medical data such as electronic health records, dynamic electrocardiogram monitoring, echocardiography video streams, chemotherapy medication time sequence data and pathological section images are collected in real time through a distributed architecture, and the storage efficiency is optimized by adopting a layered compression strategy. According to the system, risk indexes are mapped to a three-dimensional heart thermodynamic diagram, key feature contribution degrees are displayed in an associated manner, and a dynamic risk knowledge map is established to reveal a toxicity generation path. The system deploys an incremental learning mechanism to realize continuous optimization of the model, guarantees data security through differential privacy and federal learning, sets a real-time early warning feedback loop, triggers clinical intervention when the risk exceeds a threshold value, and dynamically adjusts the weight of the model according to the feedback of a physician, thereby remarkably improving the prediction accuracy and clinical applicability.

The invention belongs to the technical field of biological medicine, and particularly relates to a kit for breast cancer diagnosis and application thereof. The kit comprises an elisa plate coated with a captured antibody and an HRP labeled antibody working solution, the capture antibody is a monoclonal antibody T2X-1 of an anti-PSTPIP1 protein; the HRP labeled antibody is a monoclonal antibody Z4Y-2 of an anti-PSTPIP1 protein, and the HRP labeled antibody is a monoclonal antibody Z4Y-2 of an The heavy chain amino acid sequence of the monoclonal antibody T2X-1 is as shown in SEQ ID NO.1, and the light chain amino acid sequence of the monoclonal antibody T2X-1 is as shown in SEQ ID NO.2; the heavy chain amino acid sequence of the monoclonal antibody Z4Y-2 is as shown in SEQ ID NO.3, and the light chain amino acid sequence of the monoclonal antibody Z4Y-2 is as shown in SEQ ID NO.4. The kit for breast cancer diagnosis provided by the invention can be used as an auxiliary diagnosis means for breast cancer with high expression of PSTPIP1 protein, and has high diagnostic value.

The invention discloses an individual breast cancer risk detection method based on HRR pathway related genes, relates to the technical field of breast cancer risk detection, and aims to solve the problems of inaccurate breast cancer risk detection effect and unobvious subsequent treatment effect. According to the method, genetic typing and clinical data are combined to establish an adaptive model, relative risks are accurately calculated and graded, differential early warning and intervention schemes are formulated for low, medium and high risks, precision from risk detection to intervention is achieved, excessive medical treatment or insufficient intervention is avoided, prevention and control pertinence is improved, a dynamic tracking and optimization mechanism is established, and the risk detection accuracy is improved. Tracking frequency and indexes are set according to risk levels, intervention effects are evaluated regularly, schemes are adjusted, a'detection-intervention-tracking-optimization 'closed loop is formed, it is ensured that intervention measures continuously adapt to individual conditions, long-term risk management and control effectiveness is improved, and breast cancer prevention and control are promoted to be upgraded from static management to dynamic management.

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 invention belongs to the technical field of medical image intelligent diagnosis, and provides a breast cancer focus benign and malignant discrimination method based on a gated multi-expert mechanism. The method comprises the following steps: firstly, carrying out standardization and semantic preprocessing on a mammary gland X-ray image, a BI-RADS imaging report and structured clinical data, embedding age, mammary gland density and focus position information into a text template in a natural language form, and realizing unified expression of multi-modal input; secondly, extracting image features by utilizing a ResNet network and a simplified CLIP model, obtaining a text semantic vector by adopting a Bio-ClinicalBERT model, and establishing two sub-paths of a lump expert and a calcification expert in a Transform structure; further, an expert weight is dynamically generated through a gating routing mechanism, and soft routing fusion is executed; and finally, outputting benign and malignant results of the breast cancer focus by the binary classification module. According to the method, deep fusion and dynamic collaboration of the mammary gland X-ray image, the BI-RADS text and the clinical information are realized, and the accuracy and interpretability of breast cancer discrimination can be remarkably improved.

The invention provides an ervatamia divaricata extract as well as a preparation method and application of the ervatamia divaricata extract in resisting brain metastatic tumor. Pharmacological activity shows that the extract of ervatamia divaricata disclosed by the invention has remarkable cytotoxicity inhibition activity, and the inhibition effect of the extract is time-dependent and dose-dependent. The extract can inhibit the growth of breast cancer 4T1 brain metastases, and has no obvious adverse reaction on mice.

Belongs to the field of biological medicine, and relates to application of an anti-HER2 antibody drug conjugate to treatment of HER2 negative breast cancer. The invention also relates to application of the anti-HER2 antibody drug conjugate in preparation of a drug for treating HER2 negative breast cancer of a subject. The invention also relates to a method for treating HER2-negative breast cancer in a subject. The method comprises the step of administering the anti-HER2 antibody drug conjugate to the subject. The method is beneficial to HER2 negative breast cancer subjects, and has good safety.

The invention provides an anti-cancer active component optimization method for breast cancer treatment, which comprises the following steps: analyzing a potential interference path of an anti-cancer active component on immune system cell viability by adopting a computational chemistry simulation method according to a preliminary structural function mapping relationship, and determining a specific molecular mechanism range of immune system weakening; according to the synergistic effect evaluation result, optimizing the structural parameters of the active components through a molecular docking algorithm, adjusting the binding affinity of the active components with tumor cell targets and pathogenic bacteria targets, and determining a final structural optimization scheme; aiming at the final structure optimization scheme, verifying the expression of the modified active component on tumor inhibition and antibacterial ability by adopting simulation data of an in-vitro activity test, and obtaining a verification data set of comprehensive performance; and aiming at the updated active component design data, through a multi-objective optimization model, balancing the synergism of an anti-cancer effect and a health protection mechanism, and determining a final compound structure configuration suitable for complex requirements of a clinical environment.

The invention provides a multi-modal breast cancer image classification method for perceiving attention based on complementary information. The method comprises the following steps: acquiring a multi-modal data set formed by a mammary X-ray image and an infrared thermal image; structural features of the mammary gland X-ray image and temperature features of the infrared thermal image are extracted respectively; the structural features and the temperature features are processed through a complementary information perception attention mechanism, complementary perception features are output, and the complementary information perception attention mechanism comprises cross-modal complementary mapping: an attention weight for another modal feature is generated based on one modal feature; attention is enhanced by difference: a difference attention graph is generated based on the inter-modal feature difference; performing feature alignment and fusion on the complementary sensing features to obtain multi-modal fusion features; outputting a classification result based on the multi-modal fusion features; and training the classification network by adopting a multi-modal contrast regularization loss function, wherein the loss function comprises classification loss, feature consistency loss and contrast learning loss.

The invention relates to the technical field of breast cancer risk prediction, and discloses a breast cancer relative survival risk prediction system based on machine learning. The system comprises four core modules, wherein a data acquisition module is used for acquiring multi-dimensional clinical diagnosis and treatment records of a patient from distributed medical nodes and synchronously acquiring dynamic biomarker time sequence information in a tumor evolution process; the feature compensation module performs missing feature compensation on the diagnosis and treatment records in a cross-modal medical data fusion scene to generate clinical feature indexes, and accordingly, individual monitoring frequency is determined; the risk analysis module extracts patient diagnosis and treatment path information in a medical network, analyzes node risk associated data, and determines a survival risk level in combination with dynamic biomarker time sequence information; and the survival early warning module realizes dynamic early warning of the treatment blind area according to the individualized monitoring frequency and the survival risk level. The system can efficiently integrate medical data, accurately assess survival risks, and provide dynamic and individualized support for clinical diagnosis and treatment of breast cancer.

The invention discloses an interpretable clinical decision support system based on label generation and a knowledge graph. The method comprises the following steps: based on a breast cancer domain knowledge enhanced version Qwen-BrCaAdapt of a general large language model Qwen, analyzing an unstructured medical record text of a patient, and generating a structured result containing tags, values, evidences and explanations; calculating a reasoning label through a path matching engine by utilizing an editable structured path rule table, and matching a candidate treatment scheme according to the reasoning label; and taking the matched treatment scheme as a central node, calling a medical knowledge graph to bind entity information including clinical evidence, recommendation levels, medical insurance information, medication risks, usage and dosage, and generating a traceable JSON structure and a visual report. The method has the beneficial effects that the accuracy and efficiency of tag generation in the breast cancer field are improved, the rule maintenance cost is reduced, the interpretability and traceability of a clinical decision scheme are enhanced, and the acceptability of a doctor to a recommendation result is improved.

There are disclosed inter alia polypeptides and nucleic acids encoding said polypeptides which are useful in the treatment, prevention and diagnosis of cancer, particularly ovarian cancer, osteosarcoma, colon cancer, breast cancer, skin cancer, melanoma, esophageal cancer, esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESSC), particularly esophageal cancer, or EAC or ESSC.

The invention discloses a biomarker combination for predicting lung metastasis of breast cancer and application of the biomarker combination. The biomarker combination for predicting lung metastasis of breast cancer comprises MUC16, MAP3K13, NR4A3, FAM111A, ZNF562 and NRP1. According to the invention, a GEO database and a TCGA database are combined, a biomarker combination for predicting lung metastasis of breast cancer is obtained through analysis and screening, and potential gene characteristics closely related to development of lung metastasis of breast cancer patients are further determined by analyzing expression levels of six biomarker genes and interaction with a tumor environment. And a more effective and personalized tool is provided for prediction and treatment of breast cancer.

The invention discloses a phosphorylated peptidomimetic compound and a medical application thereof. The invention provides a phosphorylated peptidomimetic compound with a novel structure. Activity research shows that the phosphorylated peptidomimetic compound has excellent Cbl-b inhibitory activity and is an effective Cbl-b inhibitor. Technicians in the field know that Cbl-b overexpression can inhibit T cell immune response, and inhibition of Cbl-b can activate killing of an immune system on tumor cells. Therefore, the phosphorylated peptidomimetic compound or pharmaceutically acceptable salts and solvates thereof provided by the invention can be developed and prepared into drugs for treating diseases (such as non-small cell lung cancer, breast cancer, prostate cancer, head and neck squamous cell carcinoma, liver cancer, pancreatic cancer, colorectal cancer, ovarian cancer and other tumors) treated or relieved by inhibiting Cbl-b (such as non-small cell lung cancer, breast cancer, prostate cancer, head and neck squamous cell carcinoma, liver cancer, pancreatic cancer, colorectal cancer, ovarian cancer and the like) the medicine prospect is promising.

This invention belongs to the field of biopharmaceuticals and provides a keratin YK93-6, its encoding nucleic acid molecule, an expression vector containing the nucleic acid molecule, a host cell containing the expression vector or whose genome integrates the nucleic acid molecule, as well as a preparation method and a pharmaceutical composition thereof. It also provides the application of the above-mentioned keratin YK93-6 and other products in the preparation of drugs for treating benign prostatic hyperplasia, lymphoma, melanoma, analgesia, lactation, breast cancer, lung cancer, uterine fibroids, coagulation, etc.

The present provides a method of treating ovarian cancer, breast cancer, gastrointestinal cancer, lung cancer, cancer of the brain or prostate cancer in a subject in need thereof, comprising administering to the subject a first amount of a selective PARP1 inhibitor or a pharmaceutically acceptable salt thereof, and a second amount of an ATR inhibitor or a pharmaceutically acceptable salt thereof. Also disclosed are compositions and kits comprising a PARP inhibitor and ATR inhibitor.

The invention relates to the field of biomedical engineering, and discloses a breast cancer neoadjuvant chemotherapy curative effect prediction method based on a graph neural network, and the method comprises the steps: obtaining multi-modal data of a breast cancer patient, and extracting a time sequence feature vector; constructing a plurality of meta-tasks containing a support set and a query set, and constructing the time sequence feature vector of the patient sample in each meta-task into graph structure data; inputting the graph structure data into a graph neural network model for curative effect prediction and loss calculation; back propagation prediction loss update model parameters; and constructing the time sequence feature vector of a new patient to be predicted and the support set into new graph structure data, inputting the new graph structure data into the trained model, and outputting a prediction result. The system comprises a data processing module, a graph construction module, a model training module and a curative effect prediction module. According to the method, through the meta-learning strategy and the graph neural network, multi-modal time series data are effectively integrated, accurate prediction of the breast cancer neoadjuvant chemotherapy curative effect in a small sample scene is realized, and support is provided for clinical diagnosis and treatment.

The invention belongs to the technical field of biomedical functional dyes and probes, and relates to a fluorescent probe, in particular to a cyclodextrin-based near-infrared fluorescent probe, which is a conjugate formed by coating a fluorescent molecule with cyclodextrin. According to the near-infrared fluorescent probe disclosed by the invention, cyclodextrin is nested on a carbon chain of a fluorescent molecule, so that the water solubility, histocompatibility and safety are improved, the fluorescence lifetime is prolonged, the light stability is improved, and rapid transmission and response of ureters, lymphatic vessels and lymph nodes in vivo are realized; the method can be used for NIR-I and NIR-II imaging; a near-infrared fluorescence probe with targeting property can be formed by marking various tumor targeting molecules, so that accurate targeting fluorescence imaging of different types of tumors such as urogenital tumors, head and neck cancers, breast cancers and cervical cancers and metastatic lymph nodes in NIR-I and NIR-II windows is realized, and the near-infrared fluorescence probe can be applied to fluorescence imaging navigation in clinical operations.

The invention provides a breast cancer patient pCR prediction classification method based on image-gene interpretability deep learning. The method comprises the following steps: dividing an obtained data set into a training set and a test set; splitting all the three-dimensional MRI images into two-dimensional slice images; the gene text features of each breast cancer patient are classified into N different sets; segmenting a two-dimensional slice image corresponding to the training set into a plurality of patches; each patch and the gene text feature corresponding to each patch are converted into a corresponding high-dimensional Embedding (high-dimensional Embedding); respectively multiplying H by Wk and Wv to obtain K and V; multiplying G by WQ to obtain Q; obtaining a gene guidance image feature CoAttn; the CoAttn and the Q are fused to obtain a feature set F; inputting the F into a network model, and training the network model based on a loss function; inputting the two-dimensional slice image corresponding to the test set into the trained network model, and outputting a plurality of classification results; and adopting a maximum voting strategy to make decisions on the plurality of classification results. According to the method, pCR and npCR classification is carried out on the patient based on a co-attention mechanism of gene text features and MRI images, the interpretability of model decision is enhanced, and a more visual and reliable auxiliary diagnosis tool is provided for clinicians.

The invention discloses an interpretable breast cancer pathology image classification method based on dual-granularity attention and potential prototype alignment, and belongs to the technical field of artificial intelligence and medical image processing. The method comprises the steps of extracting initial features of a breast cancer pathological image by using a backbone network; respectively capturing a local discriminant feature and a cross-scale global dependency relationship through a dual-granularity multi-head attention module, and introducing a mixed pooling expert mechanism into a coarse-granularity branch to highlight a salient region; inputting the enhanced features into an optimization prototype layer, calculating the similarity with a category prototype, and outputting a classification result; when prototype mapping is carried out in the training process, a double potential space prototype projection mechanism is adopted, a candidate area closest to a prototype is searched in a shallow feature space and a deep semantic space, the prototype is projected to an optimal position, and alignment of the prototype and a semantic area is achieved. The method can effectively improve the precision and interpretability of breast cancer pathology image classification, and facilitates a clinician to understand a model discrimination basis.

The invention discloses a multi-modal information collaborative breast cancer auxiliary identification method. The method comprises the following steps: S1, acquiring a breast ultrasonic image, a breast X-ray image and clinical text data; s2, carrying out pretreatment operation; s3, segmenting the breast ultrasonic image to obtain a breast ultrasonic segmentation mask graph; s4, performing feature extraction operation on the breast ultrasonic image and the breast ultrasonic segmentation mask graph; s5, acquiring enhanced ultrasonic joint features according to feature extraction results of the breast ultrasonic image and the breast ultrasonic segmentation mask graph; s6, performing feature extraction on the mammary gland X-ray image to obtain X-ray image features; the image fusion feature is fused with the enhanced ultrasonic joint feature to obtain an image fusion feature; s7, performing feature extraction on the clinical text data to obtain clinical text features; the multi-modal features are fused with the image fusion features to obtain multi-modal features; and S8, inputting the multi-modal features into an MLP network model for cancer identification.

According to an embodiment, disclosed is a system comprising a processor configured to receive an image of a breast of a patient and patient data comprising genetic data; extract features from the image and the patient data, using one or more machine learning models, wherein the features comprise a presence of a calcification and a calcification pattern to generate a breast calcification vector; augment the breast calcification vector with the genetic data; determine, using the machine learning models, a first risk for a breast cancer; a second risk to one or more organs of the patient, wherein the organs comprises one or more of heart, kidney, lungs, pancreas, and brain; predict, a third risk based on one or more of a healing response, a tumor flow and growth, inflammation and degeneration, a disease relapse, an adverse event, and a clinical response; and determine, an overall risk to the patient.

The present application describes uses for Pertuzumab, a first-in-class HER2 dimerization inhibitor. In particular, the application describes methods for extending progression free survival in a HER2-positive breast cancer patient population; and combining two HER2 antibodies to treat HER2-positive cancer without increasing cardiac toxicity.

The invention belongs to the technical field of biological analysis and detection, and particularly relates to a photoelectrochemical-colorimetric dual-mode biosensor and application thereof in miRNA detection. According to the biosensor prepared by the invention, the bifunctional CuO-Cu2O nanoflower and the three-dimensional network g-C3N4 / AuNPs electrode are integrated, and an enzyme-assisted strand displacement signal amplification mechanism is combined, so that dual-mode collaborative detection of a target miRNA-21 is realized: in a photoelectrochemical mode, the detection limit reaches 0.35 fM (the linear range is 1 fM to 10 fM), and response is performed based on photocurrent polarity reversal; the colorimetric mode is as follows: the detection limit is 3.9 fM (the linear range is 10 fM to 10 nM), and TMB (Tetramethylbenzidine) color development visualization analysis is carried out. Single-mode interference is effectively eliminated through double-signal independent output and cross validation, and the method is suitable for high-reliability detection of trace miRNA (such as a breast cancer marker miRNA-21) in complex samples such as serum. According to the invention, the defects of insufficient sensitivity (pM level), poor anti-interference performance and high platform complexity in the prior art are overcome, and an efficient tool is provided for early diagnosis of cancers.

The invention relates to the technical field of tumor marker detection, in particular to a breast cancer lung metastasis related biomarker screening system which comprises a differential genetic factor mining module, a function association module, a verification screening module, an interference elimination module and an optimization integration module. According to the method, the candidate genetic factors highly associated with lung metastasis are extracted, and the expression fluctuation characteristics are combined to carry out difference analysis, so that the preliminary screening accuracy is improved, pathological information and pathway annotation are integrated to construct an action network, pathway weights are analyzed, and functional expressions of the candidate factors are quantitatively evaluated; an independent sample is called to verify transcription consistency and clinical relevance, interference factors with poor repeatability are eliminated, non-tumor tissue high-expression interference items are rejected in combination with background transcription characteristics, the specificity and adaptability of screening results are improved, and finally a marker set with metastasis mechanism indicating significance and prognosis prediction value is formed through comprehensive evaluation. And clinical transformation support is provided for identification of lung metastasis of breast cancer.

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 relates to the technical field of biological medicines, and discloses a PD-L1 K263 acetylation modified antibody, which is prepared from an immunogen containing a sequence as shown in SEQ ID NO.1. The PD-L1 K263 acetylation modified antibody is a PD-L1 K263 acetylation modified antibody. The invention also discloses a preparation method of the PD-L1 K263 acetylation modified antibody. The preparation method comprises the following steps: designing and synthesizing a polypeptide antigen, preparing an immunogen, immunizing animals, purifying the antibody and evaluating serum titer. The antibody provided by the invention can accurately recognize and combine with the PD-L1 protein subjected to K263 acetylation modification in the breast cancer, provides a specific tool for qualitative and quantitative detection of K263Ac-PD-L1 in the breast cancer, and provides a reliable molecular marker detection means for evaluating the curative effect of anti-PD-1 / PD-L1 immunotherapy; therefore, a biological preparation with specificity and practicability and a technical support are provided for accurate diagnosis, targeted therapy and curative effect monitoring of breast cancer.

This invention belongs to the field of biopharmaceuticals and provides a keratin YK93-8, its encoding nucleic acid molecule, an expression vector containing the nucleic acid molecule, a host cell containing the expression vector or whose genome integrates the nucleic acid molecule, as well as a preparation method and a pharmaceutical composition thereof. It also provides the application of the above-mentioned keratin YK93-8 and other products in the preparation of drugs for treating benign prostatic hyperplasia, lymphoma, melanoma, breast cancer, lung cancer, analgesia, lactation, uterine fibroids, coagulation, etc.