8774 results about "Pathology" patented technology

Disclosed is a system for detecting whether the breast cancer is benign or malignant based on multiple modalities, which comprises: an ultrasonomics model configured to extract breast tumor position information and breast tumor morphological features on the basis of an acquired grayscale breast ultrasound image, perform global interactive fusion on the breast tumor position information and breast tumor morphological feature information, and convert a Transformer encoder-based feature output into an output vector of the corresponding classification dimensions by means of a fully connected layer; a metabonomics model configured to obtain a metabolic fingerprint spectrogram and analyze the metabolic fingerprint spectrogram on the basis of a metabolic database and a predetermined significantly differential metabolite to obtain abnormal metabolic pathways, signal transduction pathways, and related biochemical reactions associated with breast cancer malignancy; and a detection module configured to perform joint modeling, based on the breast tumor position information, the breast tumor morphological features, and a peak value of the significantly differential metabolite of the breast cancer malignancy, to output a prediction result about whether the breast cancer is benign or malignant.

The invention discloses a cervical cell image intelligent diagnosis system based on a multi-modal visual language large model, belongs to the field of cervical cell image recognition, and particularly relates to the cervical cell image intelligent diagnosis system based on the multi-modal visual language large model. In order to solve the problems of low diagnosis accuracy, strong subjectivity, insufficient efficiency and lack of interpretation in the prior art, the invention provides a cervical cell image intelligent diagnosis system based on a multi-modal visual language large model. The system comprises an image block acquisition module, an image preprocessing and color normalization module, an effective tissue region screening module, a cell region extraction and post-processing module, an image end and text end processing module, a visual language large model acquisition and training module, a cell description text generation module in an inference stage, and a risk judgment and classification module. An interpretability verification and credibility evaluation module; and a structured diagnosis report output module.

This disclosure provides a method for treating a subject afflicted with a lung cancer, which method comprises administering to the subject therapeutically effective amounts of: (a) an antibody or an antigen-binding portion thereof that specifically binds to a Programmed Death-1 (PD-1) receptor and inhibits PD-1 activity; and (b) an antibody or an antigen-binding portion thereof that specifically binds to a Cytotoxic T-Lymphocyte Antigen-4 (CTLA-4) and inhibits CTLA-4 activity.

The invention relates to the technical field of prognosis analysis, in particular to a pancreatic cancer prognosis analysis system based on big data mining, which comprises a jump trend extraction module, an index grade mapping module, a sample stability screening module, a path node offset identification module and a prognosis stage classification module. According to the method, a change rate difference value and a third-order response sequence are constructed for continuous three-stage pancreatic cancer biological index sequences, jump trend feature points are extracted, and a risk grade transition sequence is formed in combination with grade span and direction judgment; multi-dimensional normalized parameters of a variable coefficient, a survival score difference value and an organ function score standard deviation are introduced to perform stable sample screening, interference of data disturbance on a path judgment result is reduced, and path mutation node positions are screened in combination with a path stability threshold value; precise classification and marking of high-risk variation stages in prognosis paths of pancreatic cancer patients are realized, stage reference is provided for individualized intervention strategies, and risk identification sensitivity and layered intervention scientificity are improved.

The invention belongs to the related technical field of brain image processing, and provides an Alzheimer's disease classification method and system based on topology perception and a group hypergraph in order to solve the problem of inaccurate classification of the Alzheimer's disease in the prior art. Constructing a dynamic function connection network sequence through a sliding window strategy; a local topology perception encoder and a global topology perception encoder are respectively used for extracting local topology features and global topology features of each time window, deep interaction and fusion are carried out, and comprehensive feature representation of a tested level is generated; according to the method, each subject is used as a hypergraph node, hyperedges are constructed on the basis of comprehensive feature representation of a subject level and by combining feature similarity calculated by diffusion tensor imaging features and clinical embedded features of the subject, then a group hypergraph is constructed, a classification result is obtained by using a hypergraph neural network, and the early classification diagnosis accuracy of the Alzheimer's disease is effectively improved.

The invention discloses a lung cancer gene mutation classification method based on frequency domain multi-scale fusion guidance, and relates to the technical field of medical image processing and gene detection. According to the MFHA mechanism provided by the invention, the pathological image is decoupled into low-frequency global and high-frequency detail sub-bands through wavelet transform, and extraction of key high-frequency features such as cell nucleus morphology and local texture is enhanced by combining multi-scale convolution and up-sampling guided by high-frequency information; the problems of insufficient feature detail mining and low feature fusion efficiency in a traditional pathological image analysis method are solved; key features are screened and focused through a channel, frequency domain-space feature deep fusion is realized through up-sampling, robust representation is constructed by combining space attention with cosine similarity and multi-dimensional statistical features, a frequency domain analysis-space focusing collaborative optimization mechanism is formed, information redundancy caused by simple feature splicing is avoided, and the robustness of the system is improved. And the classification stability of the model in a complex pathological scene is improved.

The invention discloses a breast cancer prognosis real-time evaluation system and method fusing a multi-modal image and a co-disease network, and relates to the technical field of breast cancer prognosis evaluation. According to the system, a comprehensive feature matrix fusing images, genes and clinical features is constructed by acquiring a mammary gland medical image, extracting focus features and combining gene expression information of a focus area and co-disease data of a patient, feature weighting is carried out based on an attention mechanism, and finally a prognosis risk score is output by utilizing a multi-layer perceptron model. And risk grading and intervention suggestion generation are realized. According to the system, a co-disease network and a gene interaction network are introduced for modeling, relevance expression among multi-source information is enhanced, and pathological features and systematic health status of patients can be reflected comprehensively. The intelligent level of prognosis evaluation can be improved, and good clinical popularization value is achieved.

The invention relates to a construction method of a metabolic syndrome phlegm syndrome lipid metabolism characteristic diagnosis model. The construction method comprises the following steps: S1, acquiring a serum sample; s2, carrying out lipid metabolite analysis through a full-quantitative lipidomics formula; s3, carrying out primary screening on differential metabolites; s4, optimizing the characteristic indexes by using a random forest algorithm and stepwise regression; and S5, constructing a differential metabolite diagnosis model through Logistic regression analysis. By analyzing differential lipid metabolites of patients with MetS phlegm syndromes and non-phlegm syndromes, the invention reveals that abnormal accumulation of lipid and lipid metabolites may be the core pathological parenchyma of MetS phlegm syndromes. By integrating lipid metabonomics data, using a random forest algorithm, stepwise regression and other methods to screen feature difference lipid metabolites and construct a MetS phlegm syndrome specific diagnosis model, a novel combined biomarker and evidence-based basis are provided for early diagnosis of MetS phlegm syndromes, and a scientific basis can also be provided for objective diagnosis of traditional Chinese medicine phlegm syndromes.

Aspects of the disclosure relate to complexes and other aspects relate to formulations (e.g., aqueous, lyophilized forms) comprising such complexes comprising an oligonucleotide (e.g., useful for targeting DMPK) covalently linked to an antibody (e.g., anti-TfR1 antibody).

The invention relates to the technical field of computer vision, image recognition and intelligent medicine management, in particular to a medicine bottle label content recognition method based on multiple cameras and YOLOv8. The method at least comprises the following steps: S1, deploying a medicine bottle label generation system, and generating a label; s2, multi-camera image acquisition and preprocessing; s3, carrying out chessboard calibration and space positioning; s4, medicine bottle label detection and label character recognition and structured analysis; and S5, system integration and application. According to the invention, through combination of multi-camera and multi-angle acquisition and checkerboard calibration positioning and combination with YOLOv8 label detection and OCR identification, high precision, high efficiency, end-to-end automation and system integration of medicine bottle label identification are realized, the defects of precision, efficiency, environmental adaptability and management integration in the prior art are overcome, and the system is suitable for popularization and application. The method has obvious technical advantages and practical value.

The invention discloses a gastric cancer multi-omics marker detection method, system and device, and the method comprises the following steps: S1, collecting a public database open-source space transcriptome, a single cell sequencing sample and bulk-RNAseq data for pre-processing, and collecting a primary tissue sample of a gastric cancer patient in the center for data processing; s2, integrating different modal data samples to obtain a patient label of an input end, and constructing a marker detection model and training the marker detection model; and S3, extracting a target feature value from the input external pathological section by using the trained model, and generating a diagnosis prediction result. Through multi-modal data chimerism, algorithm optimization and AI system development, subpopulation cell marker proportion prediction and prognosis diagnosis and marker evaluation with population prognosis information are realized, and an integrated diagnosis scheme for breaking through molecule-space-prognosis information is constructed.

The invention relates to an improved YOLOv8 small target detection method based on SENet, and the method comprises the steps: introducing semantic dilution loss, and measuring the dilution degree of small target features in a channel; a suppression reverse weight is generated through an SENet structure, and background redundancy is suppressed while a high response area is reserved; a C2f structure of YOLOv8 is fused, and decoupling characteristics are transmitted in cross-layer connection; in the Neck feature pyramid, a SENet response migration relation is constructed; calculating a weight deviation value, and judging whether the small target response has spatial deviation or not; position balance loss is introduced, and feature repositioning is carried out on a small target area with overlarge center-of-gravity drift; designing a channel response consistency measurement index, and measuring the SE response consistency of the small target between different epochs; sENet channel output is extracted from the image, and the variance of target area channel response distribution is counted; a channel with high variability is weakened or suppressed from a current image detection path, a detection head is introduced into a channel gating mechanism, a stable channel is adaptively selected to participate in prediction, and the small target feature representation capability is significantly enhanced.

The invention discloses a lung cancer lifetime prediction system based on prognosis factor multi-data fusion, and belongs to the technical field of lung cancer prognosis prediction, and the system comprises a multi-source data collection module which is used for collecting prognosis multi-source data of a patient; the multi-source data processing module is used for carrying out preprocessing and feature extraction on the prognosis multi-source data of the patient; the multi-source data fusion module is used for carrying out cross-modal alignment and fine-grained fusion on the extracted multi-modal feature vectors, and capturing a dependency relationship between modals based on a hierarchical attention mechanism to form patient prognosis fusion data; and the survival analysis and prediction module is used for analyzing the prognosis fusion data of the patient according to the lung cancer lifetime prediction model, automatically predicting the lifetime of the patient and displaying the lifetime in a visual form. The problems that existing lung cancer lifetime prediction is low in accuracy and cannot provide support for personalized treatment are solved. The lung cancer lifetime prediction accuracy can be improved, and support can be provided for personalized treatment.

Aspects of the disclosure relate to complexes and other aspects relate to formulations (e.g., aqueous, lyophilized forms) comprising such complexes comprising an oligonucleotide (e.g., useful for targeting DMPK) covalently linked to an antibody (e.g., anti-TfR1 antibody).

The invention relates to a novel application of animal bifidobacterium subsp. Lactis BGI-N3, in particular to an application of the animal bifidobacterium subsp. Lactis BGI-N3 in preparation of a preparation for relieving skin allergy. The invention finds that the bifidobacterium animalis subsp. Lactis BGI-N3 has a very remarkable effect in the aspect of relieving skin allergy, and the effect is specifically shown as follows: in an atopic dermatitis animal model, the dermatitis score, scratching times and percutaneous water loss are remarkably reduced, and the atopic dermatitis skin tissue damage condition is relieved; the immune organ edema condition is improved, the levels of Th2 type cell factors and inflammatory factors IL-6, TNF-alpha and IFN-gamma related to dermatitis are reduced, and the immune balance of the body is recovered; expression of fibroin and endothelial protein in skin tissue is improved, and the skin barrier is repaired; the level of antibacterial peptide in skin tissues is recovered, and the antibacterial activity of the skin is enhanced. Meanwhile, compared with a traditional chemical medicine for treating atopic dermatitis, the animal bifidobacterium subsp. Lactis BGI-N3 has no side effects such as weight loss, immune organ atrophy, immune imbalance and the like.

Aspects of the disclosure provides composition and methods for treating a subject having a blood loss condition, the method comprising administering to the subject a hemojuvelin (HIV) antagonist (e.g., anti-HJV antibody).

The invention discloses a lung squamous cell carcinoma survival prediction method and system based on SHAP interpretability, and relates to the technical field of medical data analysis. Comprising the following steps: constructing a dynamically updated physiological feature information table based on a lung squamous cell carcinoma clinical data set of a target under multiple time nodes; and associating different survival analysis models with the physiological feature information table to predict the survival probability of the lung squamous cell carcinoma in the target body, the marginal contribution of each input feature to the output of each model is obtained by using the SHAP interpretation algorithm, and the feature uniqueness score is transparently displayed in the fusion process, so that the survival probability of the lung squamous cell carcinoma in the target body can be predicted in the actual use process. The method can visually understand how each feature affects the final prediction, and achieves automatic arbitration or manual recheck through providing a model to explain conflict measurement, invalid variable elimination and pseudo high risk verification and weighted scoring, thereby guaranteeing the reasonability and safety of prediction and intervention suggestions, reducing the decision risk, and enhancing the clinical trust.

The invention relates to the technical field of treatment of chronic diseases of old people, and discloses a metabolic characteristic-based medicine dosage optimization method for chronic diseases of old people. The method comprises the following steps: acquiring drug metabolism parameters (including a plasma concentration peak value, a half-life period curve and the like) and organ function data (including a hepatocyte metabolism rate, a glomerular filtration rate and the like) of a plurality of monitoring nodes, arranging the drug metabolism parameters and the organ function data into a time sequence input vector, and extracting a dynamic feature vector by using a time convolution network and an adaptive filter network; performing feature crossing, pharmacokinetic constraint correction and feature enhancement processing, performing fusion to generate a joint feature vector, inputting the joint feature vector into a dose decision model to obtain an adjustment coefficient, and generating a drug dose interval with a safety threshold in combination with a historical drug use record. According to the method, multi-dimensional data integration and dynamic modeling are realized, the accuracy and safety of chronic disease medication of old people are improved, and the method is suitable for individualized treatment.

The invention provides a pourable tumor organoid vascularization model and a construction method thereof, and relates to the technical field of organoid culture. A temperature-sensitive degradable material and a photo-crosslinking modified temperature-sensitive material are used, the composite stent is printed through two channels, a multi-component gel mask partition curing process is combined, the stability of a perfusion pipeline is improved, and a partitioned highly-bionic complex microenvironment is accurately constructed in a macroscopic three-dimensional space; and constructing to obtain the perfusion tumor organ vascularization model. The model is high in bionic degree and stable in structure, can be applied to deepening cell and microenvironment interaction research and tumor-related drug screening, and has a good application prospect.

The invention provides a tumor treatment effect prediction method based on multi-modal data, and relates to the field of medical artificial intelligence. The method comprises the following steps: collecting magnetic resonance image data, histopathological section images and clinical baseline data of a tumor patient and a treatment response evaluation record after neoadjuvant chemotherapy; the collected image information is preprocessed; feature extraction is carried out based on the preprocessed image information; screening a feature subset with the highest prediction value from the extracted features; constructing a radiopathomics fusion model by using the feature subset with the highest prediction value; evaluating the radiopathomics fusion model; and predicting the tumor treatment effect by using the evaluated radiopathomics fusion model. The problems that an existing tumor neoadjuvant chemotherapy (NACT) curative effect prediction method is low in accuracy, poor in generalization ability, lack of multi-modal data fusion and insufficient in model interpretability are solved.

The invention relates to the technical field of infrared thermal imaging, in particular to a human body analysis method and system based on an infrared thermal image. The method comprises the following steps: acquiring an infrared thermal image sequence of a limb to be detected in a thermal relaxation process; segmenting the image sequence, constructing a topological center line of an effective contour, and defining a heat flow reference direction based on the geometric width change of a cross section along the center line; a temperature gradient vector field is calculated, and an area with the gradient direction and the heat flow reference direction in reverse distribution and containing a closed isotherm is screened out to serve as a thermal topology abnormal area; establishing a biological heat transfer model taking the surface temperature as a boundary condition, performing inverse solution on the thermal topology abnormal region, and performing inverse calculation on a heat source parameter corresponding to the subcutaneous depth; and when the heat source parameter exceeds a threshold value, generating a damage early warning. According to the method, by quantifying metabolic heat abnormality caused by inflammation in tissues, accurate positioning and quantitative evaluation of deep injury of limbs are realized, and the accuracy of human injury detection is remarkably improved.

The invention relates to the technical field of biological medicines, discloses application of circPDK1 protection, and particularly relates to application of a reagent for specific detection of circPDK1 in preparation of products for detection of esophageal cancer or prognosis evaluation of esophageal cancer. According to the application of the protected circular RNA in preparation of the esophageal cancer detection product, the circular RNA is circPDK1, and the nucleotide sequence of the circular RNA is shown as SEQ ID NO: 1. Experiments prove that circPDK1 is highly expressed in esophageal cancer tissues, growth, proliferation and migration of esophageal cancer cells are promoted, occurrence of esophageal cancer is promoted, knock-down of the circular RNA can significantly inhibit growth, proliferation and migration of esophageal cancer tumors, and the average survival time of esophageal cancer patients with low circPDK1 expression is longer than that of esophageal cancer patients with high circPDK1 expression. And a new research direction is provided for esophageal cancer detection, treatment and prognosis.

The invention provides an EGFR wild-type lung adenocarcinoma prognosis risk assessment method based on multi-omics and machine learning, and the method comprises the steps: obtaining multi-omics and clinical data of lung adenocarcinoma, obtaining a data set, and carrying out the multi-omics consensus clustering, and obtaining a molecular typing result; high-risk subtype specific candidate genes are identified, a candidate prognosis gene set is obtained, multi-algorithm machine learning comparison optimization is carried out, and a modeling strategy is obtained; performing feature screening and model training to obtain a multi-omics feature model so as to calculate an individual risk score of the to-be-tested sample; the individual risk score and the clinical staging information are utilized to obtain a clinical column diagram and a survival prediction result, then the flow of the multi-omics feature model, the individual risk score and the survival result is Web to obtain a clinical system, and a lung adenocarcinoma prognosis risk assessment result is output. The invention can realize an objective, accurate, generalizable and multifunctional prognosis evaluation and treatment guidance tool, and has important clinical application value and wide industrialization prospect.

The invention provides a breast cancer recurrence risk prediction method, system and device based on an ultrasonic image, and relates to the field of intelligent medical treatment, the method uses a deep convolutional neural network to perform deep network feature extraction on a breast ultrasonic image, and uses a deep learning semantic segmentation algorithm to perform accurate positioning and automatic segmentation on a breast tumor region of interest, thereby improving the accuracy of breast cancer recurrence risk prediction. Meanwhile, habitat analysis is carried out on the ultrasonic images to extract tumor heterogeneity features, multi-level and multi-mode features such as deep learning features, radiomics features and habitat analysis features are fused, a breast cancer recurrence risk prediction model is constructed, breast cancer recurrence risk prediction is carried out, and a breast cancer recurrence risk assessment result is output. And a quantitative basis is provided for clinical treatment decisions. The accuracy and robustness of recurrence risk prediction are remarkably improved through multi-feature fusion, and standardization and objectification of breast cancer prognosis evaluation are achieved. In addition, the method further has the advantages of being easy and convenient to operate, low in cost, noninvasive, nonradiative, good in repeatability and the like.

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 discloses a sugar chain marker combination for predicting prognosis of liver cancer transarterial chemoembolization and application of the sugar chain marker combination. The invention discovers and verifies that the combination composed of NA3Fb, NA4Fb and NA4F2b can be used as a novel marker for evaluating TACE treatment reaction and prognosis by analyzing a serum sugar chain map of a liver cancer patient for the first time. Based on the marker combination, a TACE-GT prognosis scoring model and system are established, and the progression risk of a patient within 12 months after a TACE operation can be effectively distinguished by noninvasively detecting the expression levels of three carbohydrate chains in serum of the patient and calculating a specific score. The detection method provided by the invention is based on a blood sample, and has the advantages of noninvasiveness and convenience, and a risk assessment system can provide individualized curative effect prediction and decision support for clinicians before and after TACE treatment, and has important values for realizing precise treatment of liver cancer and improving prognosis of patients.

The invention discloses a method and system for recognizing the number of bacillus in a gynecological micro-ecological microscopic image, and belongs to the technical field of image recognition and micro-ecological analysis. Performing image enhancement processing on the image to improve the definition of the target area; pixel-level segmentation is carried out on the enhanced image based on the trained image segmentation model, and a suspected bacillus target area set is extracted; extracting a morphological characteristic parameter set for each target area; screening the suspected areas by combining a bacillus morphological feature discrimination model obtained by clinical labeling sample training, and removing false targets; counting the number of the effective areas, estimating the number of bacilli in the adhesion areas by adopting a skeleton endpoint analysis method, and finally outputting the total number of bacilli in the image; the method has the characteristics of high precision, high robustness and high automation degree, and is suitable for intelligent identification of the micro-ecological structure in the gynecological microscopic image.

The method is mainly applied to the technical field of deep learning. The invention discloses an intelligent diagnosis method, device and equipment for snake venom poisoning and a medium, and the method comprises the steps: obtaining multi-modal data which comprises a wound image, a symptom text and a blood parameter; performing feature extraction processing on the wound image, the symptom text and the blood parameters to obtain image features corresponding to the wound image, semantic features corresponding to the symptom text and high-dimensional features corresponding to the blood parameters; performing alignment and fusion processing on the image features, the semantic features and the high-dimensional features through a cross-modal attention mechanism to generate fusion features; after the fusion features are input into a preset multi-task classification model, classification is carried out in a snake species label space and a toxicity category space according to the fusion features, a poisoning grade score is determined, and a diagnosis report is generated based on the poisoning grade score. According to the invention, high-precision, rapid and intelligent diagnosis of snake venom poisoning can be realized.

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 a traditional Chinese medicine chronic disease dialectical treatment optimization method based on a graph neural network, and the method comprises the following steps: S1, collecting electronic medical record text data and traditional Chinese medicine knowledge data of a patient, and constructing an initial dialectical graph; s2, obtaining an initial feature vector of a node; s3, performing multi-order spiral perception processing by using a boa convolution unit, constructing a spiral adjacent path for each target node according to a structure depth and a relation direction, and introducing a structure position coding mode to perform sequential perception modeling on a node neighborhood relation to generate an adjacent feature matrix; s4, constructing a syndrome semantic tension matrix; s5, inputting the adjacency feature matrix and the syndrome semantic tension matrix into a GATv2 model to obtain a final node feature vector; and S6, outputting a syndrome type prediction result corresponding to the patient by using the final feature vector of the node, and generating prescription and drug path recommendation. According to the method, the GATv2 model and the boa convolution unit of structure perception are combined, so that traditional Chinese medicine chronic disease dialectical reasoning and drug recommendation are realized.