104 results about "Carcinogenesis" patented technology

The invention belongs to the technical field of medical image detection, and particularly relates to an esophageal flora image intelligent detection method for risk prediction of gastroesophageal reflux disease, which comprises the following steps: acquiring flora images and clinical data, and constructing a perception network; quantifying flora displacement characteristics by using image registration; the method comprises the following steps of: extracting flora density, aggregation degree and fluorescence gradient characteristics by combining improved U-Net + + segmentation, fusing flora and epithelial cell lesion characteristics through space-time attention, generating a flora-host association map, constructing a flora dynamic evolution model by utilizing a space-time map convolutional network, predicting Barrett esophageal occurrence probability and flora diffusion trend, and predicting the occurrence probability of the Barrett esophagus. Calculating a lesion risk level through a flora imbalance quantification algorithm; in combination with reflux frequency and pH fluctuation, a flora-environment interaction model is used for simulating a field planting coefficient and correcting parameters, the parameters are transmitted to a diagnosis platform to deduce canceration risks, grading suggestions are triggered, and a visual report is generated. Therefore, the problems of long detection period, weak microscopic analysis capability and the like in the prior art are solved.

The invention relates to the technical field of novel biological nanomaterials, in particular to a lung-targeted exosome complex as well as a preparation method and application thereof. The lung targeting type exosome complex is prepared from an NK cell exosome, lipid nanoparticles and an entrapped anti-tumor nucleic acid drug, the NK cell exosome is obtained by extracting a natural killer cell NK-92MI of a human malignant non-Hodgkin lymphoma patient through a differential centrifugation method. The anti-tumor nucleic acid drug is a specific nucleic acid drug targeting a key canceration driving gene in non-small cell lung cancer. The lung targeting type exosome complex prepared by the invention is an excellent and stable drug delivery carrier, has a good lung tissue targeting function, enhances the tumor targeting effect of the exosome, also exerts the tumor cell killing function of the exosome, and has important significance for the treatment of non-small cell lung cancer.

The invention relates to the technical field of bioinformatics, in particular to an esophageal cancer RNA-protein interaction space prediction method and device based on a graph neural network, and can solve the problem that it is difficult to accurately distinguish the difference of cancer tissue and normal tissue on RPI in a traditional method to a certain extent. The method comprises the steps of obtaining multi-stage space transcriptome data related to esophageal cancer, and performing preprocessing to obtain a multi-modal feature tensor and a canceration stage tag; using the multi-modal feature tensor and the canceration stage tag, using RNA and protein as nodes, defining a static edge based on sequence complementarity and structure matching degree, defining a dynamic edge based on spatial co-expression correlation, and constructing a space-time dynamic graph updated along with the canceration stage; the space-time dynamic graph serves as input, interaction existence, site coordinates and binding energy are predicted through space-time double-branch coding and attention mechanism fusion features, and an interaction prediction result is output.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

The invention discloses a method for extracting calreticulin from tissues and application. The tissue cell extract extracted by the method contains calreticulin and can be used for preparing drugs or vaccines for preventing cancers. Experimental results show that after the tissue cell extract is immunized for several weeks, specific immune response of an organism can be triggered, cancerous cells in the organism can be removed, and therefore the aim of preventing cancers is achieved. The tissue cell extract provided by the invention can be used as a tumor preventive vaccine for preventing autologous or allogenic tumors and reducing the incidence rate of cancers in healthy people.

This disclosure describes how to treat and / or improve pancreatic cancer using a novel proteomic profile of the pancreatic cancer. In some embodiments, the treatment includes removing cancerous tissue after taking a biopsy from the patient. Optionally, the treatment includes administering anticancer agents to suppress the progression of pancreatic cancer. In further or alternative embodiments, the novel proteomic profile is used to monitor disease progression or remission.

The application discloses a liver-targeted gene editing system based on endogenous promoter hijacking and application, and belongs to the field of biological medicine. The system is composed of an LNP-wrapped modified Cas nuclease mRNA (first component) and a promoter-free viral vector carrying a therapeutic transgene donor (second component). The system uses LNP to realize the transient burst expression of Cas nuclease in the liver, mediates the generation of double-strand breaks at the site of endogenous high-expression genes, induces the site-specific integration of therapeutic transgenes without exogenous promoters, and hijacks the expression driven by endogenous promoters by using the splice acceptor (SA) mechanism. The application solves the risk of carcinogenesis caused by random integration of exogenous strong promoters and the immunotoxicity of long-term expression of nucleases through a "double safety lock" design. Experimental results prove that the system has high editing efficiency, long-term stability and no off-target, and can be used for various liver-derived metabolic diseases such as hemophilia, hypercholesterolemia and the like.

The invention belongs to the technical field of medical biological detection, and particularly relates to a gene marker combination for oral mucosa leukoplakia canceration risk and application of the gene marker combination. The gene marker combination is a combination of TP53, CASP8 and CYP2B6 genes and is obtained by screening through a WES technology, the predicted AUC of the gene marker combination reaches 0.813 (95% CI: 0.710-0.916), and the performance of the gene marker combination is remarkably superior to that of traditional pathological grading. Through Cox risk regression analysis, a high-risk group as an independent factor has significant statistical significance and is superior to traditional indexes such as age and diseased regions. The invention further provides an oral mucosa leukoplakia canceration risk prediction kit and prediction system derived from the gene combination.

The present invention discloses a reagent kit and method for detecting bladder cancer. The reagent kit includes a DHFR PCR reaction mixture, Twist1 and TSC21 PCR reaction mixtures, a point mutation positive control, a methylation positive control, and a negative control. The present invention detects point mutations in the DHFR gene and the methylation levels in the promoter regions of the TSC21 and Twist1 genes in a sample, thereby achieving combined detection of gene point mutations and methylation and determining whether or not a sample has become cancerous. The present invention is easy to operate, simple to use, has high detection sensitivity, and good specificity, and is therefore of great significance in the detection of bladder cancer.

Systems and methods for identifying a diagnosis of a cancerous state for a somatic tumor specimen of a subject are provided.SOLUTION: The method receives sequencing information comprising an analysis of a plurality of nucleic acids from a somatic tumor specimen. The method identifies a plurality of features from the sequencing information including two or more of RNA, DNA, RNA splicing, viral, and copy number features. The method provides a first subset of features and a second subset of features from the identified plurality of features as input to a first classifier and a second classifier, respectively. The method generates two or more predictions of cancer status based at least in part on the identified plurality of features from the two or more classifiers. The method combines the two or more predictions with a final classifier to identify a diagnosis of cancer status for the subject's somatic tumor specimen.SELECTED DRAWING: Figure 1

Method and device to treat cancer after surgery in the gastrointestinal tract. The device can be used in a method comprising of removing cancerous cells from a cancerous site on a tissue or organ of a patient, positioning a biodegradable device having a cancer treatment agent to cover the cancerous site, securing the biodegradable device to the cancerous site such that an outer wall of the device extends across the cancerous site, and leaving the biodegradable device at the cancerous site for treatment of microcells left adjacent to the cancerous site.

The application relates to a method for predicting gene mutation based on deep learning using multi-modal slices, image block division is performed on a digestive tract HE slice, and based on the image block division result, each image block of the digestive tract HE slice is classified into a cancerous region; the digestive tract HE slice and an immunohistochemical slice are registered; based on the digestive tract HE slice and the registered immunohistochemical slice, a gene mutation prediction training sample set is constructed, a multi-instance learning model is trained, the training standard is whether a gene is mutated, and a gene mutation prediction model is obtained; gene mutation prediction is performed according to the obtained gene mutation prediction model, a model for predicting gene mutation in combination with multi-modal pathological data of a patient is used, high specificity of the model is ensured, compared with a pathological gene prediction model using single modal data, more modal data is introduced, and the prediction accuracy is effectively improved.

The present disclosure provides compositions and methods for distinguishing between non-cancerous, pre-cancerous, and cancerous conditions in the esophagus. In particular, the present disclosure provides compositions and methods for distinguishing non-dysplastic Barrett's esophagus (NDBE) samples from pre-cancerous (e.g., low-grade or high-grade dysplasia) and / or cancerous (e.g., esophageal adenocarcinoma) samples based on methylation status and / or DNA copy number abnormalities.

The invention provides a construction method and application of an endometrial cancer CT image segmentation model. The construction method comprises the following steps: acquiring a plurality of endometrial CT images marked with canceration regions as a training data set; constructing an endometrial cancer CT image segmentation framework, wherein the endometrial cancer CT image segmentation framework is of a U-Net structure comprising a coding unit, a bottleneck layer and a decoding unit; and training the endometrial cancer CT image segmentation architecture by using the training data set to obtain an endometrial cancer CT image segmentation model. According to the scheme, multi-scale structure change information is explicitly captured by constructing a multi-direction coding structure response operator and generating a coding structure gradient field, and representation of thin and weak boundaries and small lesions is enhanced to improve the segmentation accuracy of the model.

The invention relates to the technical field of near-field imaging, in particular to a papillary thyroid carcinoma identification method based on terahertz imaging. According to the technical scheme, the method comprises the following steps that two sets of sections at the same position of the thyroid tissue of a patient are obtained, one set is stained sections, and the other set is unstained sections; and comparing the two groups of slices through an optical microscope, observing the position corresponding relation of the two groups of slices, and selecting an observation area based on cell morphological characteristics. According to the invention, synchronous acquisition of tissue apparent morphology and internal structure under nanoscale resolution is realized through the terahertz near-field imaging system, automatic analysis of an undyed thyroid section can be completed within 30 minutes by combining multi-modal feature fusion and a target detection algorithm, objective positioning and diagnosis scores of a cancerous region are directly output, and the accuracy and accuracy of diagnosis of the cancerous region are improved. And rapid, lossless and quantitative papillary thyroid carcinoma identification is realized.

The invention discloses an application of a TNFRSF12A inhibitor in preparation of a medicine for treating oral squamous cell carcinoma. Based on single cell transcriptome data, oral squamous cell canceration related tissues are divided into four tissue ecological subtypes, and a tumor sample is divided into two subtypes ET3 and ET4. The ET3 subtype is a lymphocyte infiltration type, and shows CD8 + T cell infiltration, immune activation pathway activity enhancement and high expression of CD3, CD8A and PDCD1; the ET4 subtype is a lymphocyte rejection type, is enriched with COL1A1 + fibroblasts, and is accompanied by high expression of TNFRSF12A, COL1A1 and CD276. According to the application disclosed by the invention, the expression of the TNFRSF12A in the ET4 subtype is obviously improved, in-vivo experiments prove that knockout or blocking of the TNFRSF12A can promote lymphatic T cell infiltration and inhibit tumor growth, and a stronger inhibition effect is shown when the knockout or blocking of the TNFRSF12A is combined with an immune checkpoint inhibitor. The invention provides a system for typing oral squamous cell carcinoma.

The present application relates to the field of tumor detection, and in particular to a biological sample analysis method and system based on nuclear pore distribution patterns. The method provided by the present application comprises: S101, obtaining microscopic image data of a biological sample; S102, obtaining a first region of interest, a second region of interest and a third region of interest; S103, drawing a nuclear membrane edge curve; S104, extracting nuclear pore distribution-nuclear membrane curvature distribution data of cells in the biological sample; S105, identifying the nuclear pore distribution pattern of the cells in the biological sample; S106, evaluating the state of the tissue or organ corresponding to the biological sample. Based on the identified nuclear pore distribution pattern of individual cells in the biological sample, the present application can evaluate the state of the tissue or organ corresponding to the biological sample according to the overall nuclear pore distribution pattern of the cells in the biological sample, such as whether it is a tumor sample, whether it has a tendency to cancer, etc., which is of great significance for early diagnosis of tumors.

The present invention relates to a composition comprising a TAF10 inhibitor for preventing or treating colorectal cancer. The inhibition of TAF10 was found to lead to suppressing the growth and proliferation of colorectal cancer cells, reducing vascularization, and inhibiting the WNT signaling pathway, and increase the survival rate of patients as analyzed with a large-scale cancer database. In particular, reversal of the cancerization process, which is the differentiation of colorectal cancer cells into normal cells, was confirmed. Unlike conventional anticancer agents that simply induce the death of cancer cells, the present invention can thus be advantageously utilized as a treatment method for converting colorectal cancer cells into normal cells while excluding side effects of normal cell death that may occur during anticancer treatment.

This invention relates to the field of pharmaceutical technology, specifically proposing the application of ligustilide in the preparation of drugs for inhibiting and / or preventing and / or alleviating and / or treating lung tumors to address the current lack of natural drugs for lung cancer prevention. Lung tumors are nitrosamine-induced carcinogenesis of lung tissue. The invention relates to a pharmaceutical preparation for inhibiting and / or preventing and / or alleviating and / or treating lung tumors, comprising an active ingredient and pharmaceutically acceptable excipients; wherein the active ingredient is ligustilide. Ligustilide directly targets the STING protein, activates the cGAS-STING pathway, promotes STING phosphorylation and cell-mediated immune surveillance, while simultaneously regulating ROS homeostasis and the inflammatory microenvironment, reducing nitrosamine damage to lung epithelial cells, and preventing nitrosamine-induced lung carcinogenesis.

This invention discloses a high-performance computing-based system and method for evaluating colorectal cancer screening strategies, belonging to the field of medical information processing technology. It includes: a dual-pathway disease model module supporting both adenoma-carcinoma pathways and serrated adenoma pathways, modeled using age-dependent normal distribution progression time; an individualized risk scoring module integrating risk factors such as family history, inflammatory bowel disease, obesity, and diabetes; a screening strategy simulation module supporting combined screening strategies using different tools, employing compliance parameters specific to the Chinese urban population; a high-performance computing module using Numba JIT compilation technology for acceleration, multi-process parallel processing for further acceleration, and data structure optimization such as integer age to reduce memory usage; and a health economics evaluation module calculating indicators such as QALY, LYG, and ICER. This invention achieves significant performance improvements while maintaining model accuracy, greatly reducing simulation time for large-scale populations.

The invention discloses an oral potential malignant disease canceration time prediction system, electronic equipment and a medium, and the system comprises a data acquisition module, a data division module, a model construction module, a model training module and a time prediction module. And the risk prediction module is used for predicting the cancerization risk of the patient by using the artificial intelligence model with the best prediction accuracy, and determining the risk values of cancerization in the first time period and the second time period, or determining the time period with the higher cancerization possibility. According to the technical scheme, the canceration risk prediction probability values of the patient in multiple time periods are innovatively output by adopting the artificial intelligence model in combination with an oversampling technology and ending weight adjustment, a more comprehensive and specific prediction result can be provided for the patient, and personalized follow-up visit and formulation of a treatment scheme are facilitated.

The present application belongs to the technical field of medical image detection, and particularly relates to an esophageal flora image intelligent detection method for gastroesophageal reflux disease risk prediction, wherein flora image and clinical data are acquired to construct a perception network; flora displacement features are quantified by image registration; flora density, aggregation degree and fluorescence gradient features are extracted by improved U-Net++ segmentation; flora and epithelial cell lesion features are fused by space-time attention to generate flora-host correlation atlas; flora dynamic evolution model is constructed by space-time graph convolution network to predict Barrett esophagus occurrence probability and flora diffusion trend; lesion risk grade is calculated by flora imbalance quantification algorithm; in combination with reflux frequency and pH fluctuation, flora-environment interaction model is used to simulate colonization coefficient and correct parameters, which are transmitted to a diagnosis platform to deduce cancer risk, trigger grading suggestions and generate a visual report. Thus, the problems of long detection cycle and weak microscopic analysis capability in the prior art are solved.

The embodiment of the invention provides a primer probe combination for endometrial cancer gene methylation detection and application of the primer probe combination. According to the PCR primer probe combination for endometrial cancer gene methylation detection provided by the embodiment of the invention, the specific primers and probes are designed, so that different cancerization stages and subtypes of endometrial cancer can be covered, and meanwhile, the sensitivity and specificity of the kit are ensured; therefore, a basis is provided for early screening, accurate detection and individualized treatment of the endometrial cancer. The PCR primer probe combination provided by the embodiment of the invention has the advantages of covering different canceration stages and subtypes of endometrial cancer and ensuring the sensitivity and specificity of the kit.

The present application relates to the field of cancer cell identification, and particularly relates to a method and system for identifying different types of cancer cells of a renal pelvis cancer patient based on urine, the method comprising: collecting a urine sample, obtaining a detection sample by using the urine sample; sequencing the detection sample to obtain a cell expression profile of the detection sample; obtaining cell expression profile data of a mixed urothelial cell subpopulation by using the cell expression profile, the mixed urothelial cell subpopulation comprising normal urothelial cells and cancerous urothelial cells; extracting different proto-oncogene expression data from the cell expression profile data of the mixed urothelial cell subpopulation; analyzing the different proto-oncogene expression data to obtain a cancer cell ratio and identify different types of cancer cells. The present method quickly and accurately obtains different types of cancerous urothelial cells through non-invasive examination and proto-oncogene expression profile data, and provides a reference and basis for preoperative neoadjuvant therapy of renal pelvis cancer.

The present invention concerns the field of cancer and methods for early detection of cancerous lesions. Specifically, the invention relates to a method for early detection of cancerous lesions through the analysis of microsatellite instability in the genome of an individual from a liquid biopsy.

The application provides a method for constructing an endometrial carcinoma CT image segmentation model and application thereof, including the following steps: acquiring multiple endometrial CT images labeled with cancerous regions as a training data set; constructing an endometrial carcinoma CT image segmentation architecture, wherein the endometrial carcinoma CT image segmentation architecture is a U-Net structure including an encoding unit, a bottleneck layer and a decoding unit; and training the endometrial carcinoma CT image segmentation architecture with the training data set to obtain an endometrial carcinoma CT image segmentation model. The scheme explicitly captures multi-scale structural change information by constructing a multi-directional coding structure response operator and generating a coding structure gradient field, thereby strengthening the representation of thin and weak boundaries and small lesions to improve the segmentation accuracy of the model.

The invention discloses a polysaccharide-polyphenol-metal compound for preventing colorectal cancer related to colitis, the polysaccharide-polyphenol-metal compound is formed by compounding a metal-polyphenol nano ligand and polysaccharide molecules, and the metal-polyphenol nano ligand is a nano complex formed by tea polyphenol and metal ions through coordination. The invention also discloses a preparation method and application thereof. The compound has the functions of oxidation resistance, inflammation resistance, immune metabolism reprogramming, intestinal flora steady-state regulation and control and the like, and can activate an Nrf2-ARE antioxidant pathway and restore expression of epithelial tight junction protein by adjusting polarization of macrophages and inhibiting NF-kB / STAT3 proinflammatory signals, thereby promoting reconstruction of beneficial flora (such as Lactobacillus and Akkermansia). The composite system can also achieve the synergistic effect of immunoregulation and chemotherapy combined cancer prevention under the drug loading condition, and the canceration process of colitis is remarkably delayed.

The invention provides a cervical lesion thermodynamic diagram prediction method and system based on pathological base model fine tuning, a storage medium and a program, and relates to the technical field of medical pathological diagnos.The method comprises the steps that S10, a pre-trained Prov-GigaPath tissue pathological base model is adopted to extract pathological feature vectors of a cervical tissue panoramic digital slice image; s20, executing dichotomy mapping according to the pathological feature vectors, and generating positive probabilities of the sub-regions; s30, generating a highly suspicious sub-region set according to the positive probability of each sub-region; and S40, executing five-classification prediction according to the height suspicious sub-region set, and generating lesion level probability distribution corresponding to each height suspicious sub-region. Compared with the prior art, the method has the following beneficial effects: 1, two-classification and five-classification tasks are decoupled, so that the model complexity is reduced; secondly, fine-grained distinguishing of CINI-III and canceration is achieved through a five-classification grading framework; thirdly, by embedding an uncertainty quantification mechanism, a lesion grading result and model confidence degree evaluation are synchronously output.

In the disclosed systems and methods for characterizing a cancer condition of a tissue in a subject, a computer system inputs information into an ensemble model. The information includes, for each respective class of radiomics features in a plurality of classes of radiomics features, a corresponding value for each respective radiomic feature in a corresponding plurality of radiomics features of the respective class of radiomics features obtained from a medical imaging dataset. The ensemble model comprises a plurality of component models. The computer system obtains as output from each respective component model in the plurality of component models a corresponding component prediction for the cancer condition, thereby obtaining a plurality of component predictions for the cancer condition. The computer system combines the plurality of component predictions to obtain as output of the ensemble model a characterization of the cancer condition.

The present invention provides a system and method for identifying cancerous tissue based on analysis of histopathologic slides of prostate tissue. In certain embodiments, the system and method classify image information associated with a histopathologic slide based on cancer risk using a first machine learning algorithm trained using a first training set and providing mask information associated with cancer risk that is superimposed on the image data to highlight cancerous or high risk tissue.