67 results about "Clinico pathological" patented technology

The invention belongs to the technical field of medicine, and discloses a method and system for predicting anastomotic fistula occurring after colorectal cancer operation, and the method comprises the steps: obtaining clinical pathology data and laboratory analysis data of a patient; filling missing data in the clinical pathology data and the laboratory analysis data; screening key indexes by using a single-factor regression analysis technology; performing multi-factor regression analysis on the key indexes, and screening to obtain independent prediction factors for predicting whether the colorectal cancer patient has the anastomotic fistula or not after operation; based on the independent prediction factors obtained through screening, an anastomotic fistula prediction model is constructed and trained, and the trained anastomotic fistula prediction model is used for predicting the postoperative anastomotic fistula occurrence probability of the colorectal cancer patient. The method can more accurately predict the probability of occurrence of anastomotic fistula of a colorectal cancer patient after an operation, has better accuracy and reliability, can facilitate preoperative prediction of the probability of occurrence of anastomotic fistula for clinical doctors, and assists in guiding treatment decision.

The invention is applicable to the technical field of pathological analysis, and provides a biological sample pathological detection method and system based on a characteristic signal threshold, and the detection system comprises: a molecular probe labeling module for performing molecular probe labeling on a standard sample and a biological sample to be detected; the three-dimensional gel electrophoresis device is used for carrying out three-dimensional gel electrophoresis separation on the marked sample; the three-dimensional gel electrophoresis signal collection module is used for extracting and collecting sample signals after electrophoretic separation; the tissue characteristic signal threshold establishing module is used for establishing a characteristic signal threshold of the standard sample; and the automatic diagnosis module is used for detecting the biological sample to be detected by utilizing the established characteristic signal threshold. The biological sample pathology detection method based on the characteristic signal threshold value shows excellent performance in clinical pathology detection, the clinical pathology work burden can be relieved, the clinical pathology work accuracy can be improved, and the biological sample pathology detection method has wide application prospects and huge market potential.

The invention relates to a pathological section intelligent auxiliary differential diagnosis system based on multi-modal fusion, in particular to the field of clinical pathology, semantic unification of multi-modal data is achieved through meta-task construction and a cross-modal alignment technology, and transferable diagnostic knowledge is extracted by utilizing a meta-learning framework; the method combines a generative model and knowledge constraints to generate high-quality synthetic data, and finally fuses real and synthetic samples through a self-adaptive diagnosis mechanism, thereby remarkably improving the differential diagnosis capability of rare lesions, effectively solving the problem of model generalization in a training data scarcity scene, and improving the accuracy of model identification. And efficient and reliable intelligent auxiliary decision support is provided for clinical pathological diagnosis.

The invention discloses a colorectal cancer postoperative venous thromboembolism risk prediction method and system, and the method comprises the steps: obtaining clinical pathology data of a historical patient before the patient receives a total proctectomy, carrying out the preprocessing of the clinical pathology data, employing a standardized score to detect an abnormal value in the clinical pathology data, carrying out the filling of a missing value, and carrying out the prediction of the risk of the venous thromboembolism. Performing standardization processing on the continuous variables; based on the preprocessed clinical pathology data, extracting clinical pathology features by adopting Lasso regression and grid search; constructing a Stacking integration model based on a random forest, a CatBoost model and logistic regression, and training the Stacking integration model by using the extracted clinical pathological features; and by using the trained Stacking integrated model, processing clinical pathology data of a target patient before the target patient receives the total proctectomy to obtain a prediction result. The colorectal cancer postoperative venous thromboembolism risk can be effectively predicted, and support is provided for clinical decision making.

The invention provides a lung cancer brain metastasis identification method and system based on multi-modal data, and the method comprises the steps: obtaining clinical pathology data, lung CT image data and brain multi-parameter MRI image data of a user, and carrying out the data preprocessing; performing anomaly detection on the preprocessed data to obtain a corresponding anomaly score; weighting various abnormal scores, and performing normalization processing to obtain a comprehensive abnormal score; determining a prediction threshold value of the brain metastasis risk of the lung cancer according to the maximum Youden index; comparing the comprehensive abnormal score with a prediction threshold, and determining a prediction result; when the prediction result shows that the user is a lung cancer brain metastasis patient, dividing the preprocessed brain multi-parameter MRI image data into a plurality of areas with preset sizes, and respectively calculating reconstruction errors; and mapping the reconstruction error of each region to generate an error mapping graph so as to reflect the high-risk region of the lung cancer brain metastasis.

The invention discloses a pathology grading method and device, a storage medium and a computer program product, and relates to the technical field of medical image processing.The pathology grading method comprises the steps that a light microscope image of renal biopsy is obtained, a preset glomerulus recognition model is used for detecting the light microscope image, and slice images of all glomerulus are obtained; according to the slice image, determining a proportion result of each type of glomerulus in the light microscope image; and determining a pathology grading result according to the proportion result and a preset pathology grading rule. According to the method and the device, the glomerulus in the biopsy image is automatically identified and classified, and automatic classification is carried out according to the clinical pathology classification standard, so that the workload of a clinician in pathology classification is reduced, and the accuracy and the efficiency of pathology classification are improved.

The invention provides a puncture sample detection platform based on a tumor protein targeting probe. According to the puncture sample detection platform, IR-780 is used for marking tumor specific protein in a puncture sample; the improved three-dimensional gel electrophoresis device is used for separating proteins in tissues; the gel processing platform is used for processing and analyzing the separation gel medium; and the automatic diagnosis platform is used for automatically judging benign and malignant conditions of the puncture sample according to the intensity and coherence of the fluorescence signal and circling the contour of the malignant sample. Compared with clinical pathological examination, the method has the advantages that benign and malignant puncture samples are judged by evaluating the protein content of various tumor receptors, and the problem of diagnosis difficulty caused by limited histological information is well solved. Meanwhile, the dependence on visual diagnosis of pathological analysis of the frozen section and experience of pathologists is reduced, and the accuracy and efficiency of pathological tissue diagnosis are effectively improved. The automatic diagnosis platform can eliminate a large number of benign samples and can reduce repeated work of pathologists.

The invention discloses a prognosis evaluation method and system for diffuse large B-cell lymphoma. According to the method, firstly, a gene module closely related to lipid metabolism is screened from DLBCL transcriptome data through weighted gene co-expression network analysis (WGCNA), and then eight key prognosis genes including FNDC1, IL22RA2, C15orf48, OMD, MFAP2, BC017398, CXCL6 and TNFAIP6 are identified from the module by adopting multi-step regression analysis (single factor Cox, LASSO and multi-factor Cox). A risk scoring model is constructed based on the expression levels and regression coefficients of the genes, and DLBCL patients can be divided into a high-risk group and a low-risk group with significant survival differences. The risk score and the clinical pathological factors are further integrated to construct a column graph, and individualized survival probability prediction can be achieved. The invention further provides a corresponding prognosis evaluation system, electronic equipment and a storage medium. An independent data set verifies that the prognosis model has excellent prediction performance and clinical practical value.

The invention discloses an application method for revealing RIPK3 as a pathological index of osteoarthritis based on transcriptomics analysis, and belongs to the technical field of molecular biology and genetic engineering.The method comprises the following steps that S1, a Trizol reagent is used for conducting total RNA extraction on an articular cartilage specimen, and the RIN number gt is selected; 7.0, constructing a sequencing library by using the high-quality RNA sample; s2, carrying out quality inspection on the constructed library, and carrying out mRNA sequencing by using an Illumina NovaseqTM6000 sequencer after all the libraries are qualified in quality inspection; s3, carrying out bioinformatics analysis on data generated by high-throughput sequencing; s4, different parts of the cartilage tissue of the OA patient are labeled with RIPK3 through immunofluorescence, it is proved that expression of the RIPK3 in cartilage cells has strong correlation with the pathology of the OA focus, and the feasibility of taking the RIPK3 as a clinical pathology index is proved.

The invention discloses a medical synthetic data analysis method and device based on causal reasoning and a medium. The method comprises the following steps: generating medical synthetic data through a generator created according to a real clinical data set; analyzing the real clinical data set and the medical synthetic data to obtain a first causal diagram and a second causal diagram, and comparing the two diagrams to identify an abnormal causal edge corresponding to the medical synthetic data; determining a path corresponding to the abnormal causal edge in the first causal graph, extracting risk factors and corresponding results in the path, and respectively calculating an average processing effect and an effect difference value of the risk factors on the results in the real clinical data set and the medical synthetic data; and determining an analysis result of the medical synthetic data according to the effect difference value. According to the method, the medical synthetic data quality can be deeply analyzed, the fidelity of the synthetic data to a real-world clinical pathology causal mechanism is verified, the specific link of causal distortion is accurately positioned, and clear and executable guidance is provided for the correction data generation process.

The invention relates to a liver cirrhosis prediction and noninvasive diagnosis system and method for autoimmune hepatitis, and belongs to the technical field of medicine. The system comprises a data acquisition module, a data processing module and a model construction module. The data acquisition module is used for acquiring blood detection indexes of a patient, including total protein, albumin, total bilirubin, direct bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), glutamyltranspeptidase (GGT), urea, creatinine, platelet count (PLT) and the like. The model building module is combined with blood detection data and clinical pathology data, a statistical analysis method such as binary Logistic regression is adopted, and a liver cirrhosis prediction model is trained and built. Based on four key blood detection indexes, the method can effectively predict the occurrence of liver cirrhosis of AIH patients, and especially provides a scientific reference for noninvasive diagnosis and curative effect monitoring for patients who cannot perform liver puncture or reject liver puncture.

The invention provides an innovative model based on the correlation between the PLEKHA4 gene expression level and the low-grade glioma radiotherapy sensitivity and a prediction method. According to the model, a multi-factor Logistic regression analysis framework is constructed, and the PLEKHA4 gene expression level and key clinical pathological parameters are organically combined, so that a radiotherapy sensitivity prediction model is established. The method comprises a series of steps of sample collection, gene expression detection, clinical information collection, model calculation, result interpretation and the like, and can realize accurate prediction of radiotherapy response of low-grade glioma patients. Compared with the prior art, the method has the remarkable advantages of simplicity and convenience in operation, high prediction accuracy, high clinical transformability and the like. The method has great potential in the aspect of guiding individualized radiotherapy scheme formulation, can significantly improve the treatment effect, and has important value for medical application. Besides, the model can be optimized through further clinical verification, so that clinical practice can be better served, and the life quality and prognosis effect of patients are improved.

The invention provides a clinical application level pathology large model construction method and system based on weak supervised learning. The method comprises the steps of performing weak supervised training on each historical clinical pathology image, adjusting an upstream learning parameter of a large model according to a training result, constructing an upstream task of the large model, and performing segmentation decoding on each weak supervised training result, determining a segmentation result, adjusting downstream learning parameters of the large model, constructing a large model downstream task, obtaining upstream fine features and downstream fine features, constructing an upstream and downstream cooperative task of the large model, and generating a pathological large model in combination with the large model upstream task and the large model downstream task. And inputting the current clinical pathological image into the large pathological model for pathological information identification, obtaining a plurality of pathological identification tags corresponding to the patient, and generating a pathological report of the patient, thereby reducing the calculation complexity when processing a high-resolution image, optimizing a collaboration mechanism among different tasks in the large model, and improving the efficiency of the large model. The method can be widely applied to pathological recognition technologies.

The invention relates to the technical field of animal model construction, and particularly discloses a construction method of an atherosclerotic plaque thrombosis model. Comprising the following steps: 1) selecting carotid artery constricted ApoE- / -mice fed with high-fat feed; and 2) injecting LPS (Lipopolysaccharide) into the abdominal cavity to obtain the atherosclerotic plaque thrombosis animal model. The construction method provided by the invention is simple to operate and high in repeatability, atherosclerotic plaque thrombus formation can be induced under the stimulation of low-dose LPS, the thrombus occurrence rate equivalent to the thrombus occurrence rate in the prior art can be obtained, the construction method can be closer to the formation of atherosclerotic thrombus related to clinical endotoxemia, and the clinical pathological process can be better simulated.

The invention provides a method for predicting value of breast cancer neoadjuvant chemotherapy. The method comprises the following steps: S1, collecting information of a patient who is diagnosed as breast cancer for the first time and is subjected to neoadjuvant chemotherapy; s2, performing MAT condition analysis on different clinical pathological characteristic groups; s3, checking the curative effect of neoadjuvant chemotherapy of the patient; s4, comparing the prediction effects of the MAT, the TG and the TC by using an ROC curve; on the basis of 221 patients diagnosed with breast cancer parallel neoadjuvant chemotherapy from January 2019 to December 2020, MAT is constructed through statistical analysis to predict the relation between breast cancer neoadjuvant chemotherapy effects, evaluation of the breast cancer neoadjuvant chemotherapy effects is facilitated, accurate evaluation of neoadjuvant chemotherapy reaction is achieved, and the clinical application prospect is broad. Important information is further provided for the influence of systemic treatment on breast cancer biology and prognosis.

The application provides a virtual restaining method of pathological section images, comprising the following steps: obtaining a histopathological image of a pathological section; constructing a virtual restaining network of the histopathological image based on a diffusion model; training the virtual restaining network, and taking the trained virtual restaining network as a virtual restaining model of a pathological image; segmenting the histopathological image into a plurality of segmented images; virtually restaining the segmented images through the virtual restaining model of the pathological image to obtain restained segmented images; and splicing the restained segmented images to obtain a restained histopathological image. The application can directly generate an IHC image from a scanned HE staining image, avoids time, money, manpower and material resources spent in the IHC section manufacturing process, makes clinical pathological diagnosis more convenient, and promotes the development of the medical and health industry.

This invention relates to the field of medical technology, specifically to a method for preoperative risk assessment and prediction in liver transplant patients with hepatocellular carcinoma, comprising the following steps: Sample collection: selecting plasma samples and corresponding clinicopathological information from liver transplant recipients of hepatocellular carcinoma, and clarifying the inclusion and exclusion criteria for samples; Plasma cell-free DNA extraction and whole-genome sequencing: extracting and quality-controlling cell-free DNA from the plasma samples collected in step S1, constructing a sequencing library, and performing low-coverage whole-genome sequencing. This invention utilizes plasma-extracted cfDNA for whole-genome sequencing, combined with clinical testing information, to construct a preoperative risk assessment and prediction model for postoperative recurrence in liver transplant recipients of hepatocellular carcinoma based on non-invasive testing. This model can be used to predict the probability of recurrence-free survival before liver transplantation. The model derivation cohort integrates clinical records and circulating tumor DNA data for preoperative recurrence risk prediction.

The invention discloses an inflammatory bowel disease curative effect prediction system based on multi-modal data, and relates to the field of medical information processing. The system comprises a multi-modal data acquisition module used for receiving multi-modal data; the text feature extraction module is used for determining a text feature vector by utilizing a text feature extraction model; the endoscopic intestinal mucosa feature recognition module is used for determining intestinal mucosa features by using an endoscopic intestinal mucosa feature recognition model; the image feature recognition module is used for determining iconography features by using an image feature recognition model; the pathological feature extraction module is used for determining pathological features by using the pathological recognition model; and the curative effect prediction module is used for predicting the curative effect of the biological agent based on the multi-modal data characteristics by using a Transform model and a full connection layer. Compared with the prior art, biological agent curative effect prediction based on clinical-pathology-endoscope-image multi-mode feature fusion is achieved, and the accuracy and clinical rationality of diagnosis and treatment decisions are improved.

The invention provides a premature ovarian insufficiency (POI) animal model and a construction method thereof, and particularly, the method utilizes chronic unpredictable pressure stress to construct different pathological stage models of mouse POI, and comprises the following steps: screening experimental animals with normal ovarian functions, adaptively feeding, applying various pressure stresses to the experimental animals, and obtaining the POI animal model. The POI model has a compensatory period, and is more in line with the clinical pathological process of patients suffering from premature ovarian insufficiency caused by common pressure in modern society.

The present invention relates to the application of toxicogenomics methods to the detection and / or prediction of histopathological abnormalities in human or animal subjects based on clinical pathology data. It has been observed that reliable results can be obtained even in the absence of image data. A computer-implemented method for predicting the presence of a histopathological abnormality in an organ of a human or animal subject based on clinical pathology data comprises receiving clinical pathology data obtained from the human or animal subject, applying an analytical model to the clinical pathology data, the analytical model being configured to output a result indicative of the likelihood of the presence of the histopathological abnormality in the organ of the human or animal subject, and outputting the result.

The present application relates to the technical field of animal model construction, and specifically discloses a method for constructing an atherosclerotic plaque thrombosis model. ‑ / ‑ The method comprises the following steps: 1) selecting an ApoE The method provided by the present application is simple in operation, high in repeatability, and can induce atherosclerotic plaque thrombosis under the stimulation of a low-dose LPS, and can obtain a thrombosis rate comparable to that of the prior art, and can be closer to the formation of atherosclerotic thrombosis related to clinical endotoxemia, and better simulate the clinical pathological process.

The invention provides a children nephroblastoma machine learning survival model developed based on a center queue, and belongs to the technical field of nephroblastoma prediction and evaluation. And analyzing and evaluating prognosis risk factors of the nephroblastoma, establishing an interpretable machine learning survival model based on the central queue, and finally, converting the model into a clinically friendly online prediction platform to promote the transformation of a prognosis evaluation tool from scientific research to clinical practice, so as to improve the prognosis evaluation efficiency. The method aims at providing dynamic risk assessment and individualized treatment decision support for clinicians, and finally the survival income of relapse / refractory child patients is improved.

The invention relates to the technical field of medical image processing and artificial intelligence, and particularly discloses an intestinal cancer pathological image screening method applied to fluorescence immunohistochemistry. The method comprises the following steps: carrying out multi-channel acquisition and pretreatment on a fluorescent pathological section; according to the method, the image screening model based on the convolutional neural network is constructed and trained, automatic screening and grading of pathological images are realized through the trained deep learning model, mass fluorescence immunohistochemical intestinal cancer pathological images can be processed in batches, manual evaluation one by one is not needed, the screening period is greatly shortened, and the screening efficiency is improved. The problems that traditional manual screening is low in efficiency and difficult to meet high-throughput image analysis requirements are solved. According to the automatic process, the workload of pathologists is reduced, fatigue errors caused by long-time manual screening are avoided, efficient and stable data screening support is provided for subsequent accurate quantitative analysis and AI model training, and efficient promotion of clinical pathological research and diagnosis is assisted.

The invention relates to an immunohistochemical detection reagent and kit for bladder cancer neuroendocrine subtype diagnosis and prognosis based on a PHF6 index. The reagent comprises a PHF6 primary antibody, a PHF6 secondary antibody, an antigen repair solution, blocking serum, a DAB developing solution, a hematoxylin staining solution, a hydrochloric acid-alcohol differentiation solution, an ammonia water blue-returning solution and a neutral gum mounting medium. The obvious correlation between the expression level of the PHF6 and the neuroendocrine subtype of the bladder cancer is determined by detecting the expression condition of the PHF6 in the bladder cancer tissue and combining a series of cell experiments and clinical pathological analysis. The invention provides a novel and effective diagnosis means which can help a clinician to accurately judge the neuroendocrine subtype of bladder cancer, so that a more accurate individualized treatment scheme is formulated for a patient, and more reliable prognosis evaluation is provided.

The present invention discloses a staining method for co-staining of three fibrous tissues, which includes the steps of successively using Victoria blue staining and Gordon-Sweets silver ammonia solution staining on the processed tissue sections; the staining method of the present invention combines Victoria blue staining and silver ammonia solution staining, and can clearly present the staining of three fibrous tissues on the same section, clearly show the relationship between the cell nucleus structure and elastic fibers, significantly improve the diagnostic accuracy of the degree of tumor invasion, reduce the demand for tissue samples, and the staining method of the present invention has the advantages of clear color contrast, rich levels, accurate positioning, simple operation, etc., and has important significance for clinical pathological diagnosis and is worthy of popularization and application.

The utility model relates to the technical field of medical instruments, in particular to a material taking knife with a protection function for clinical pathological diagnosis, which comprises a knife handle and a knife shell, the knife handle is connected to one end of the knife shell, the knife shell is hollow, a blade is connected in the knife shell in a sliding mode, the blade is provided with a pull groove, a pull rod is arranged in the pull groove in a penetrating mode, and the knife handle is connected with the knife shell. The knife shell is provided with a clamping cavity, and the two ends of the pull rod penetrate through the clamping cavity. The two sets of pull rods are arranged at the two ends of the blade respectively to improve the stability of the blade. The blade can be telescopically adjusted and can be retracted into the knife shell when not in use, so that potential safety hazards are avoided; during sampling, the convex particles arranged on the outer wall of the knife handle increase friction to prevent the sampling knife from falling off during sampling; when hard materials are taken, the pressing plate can be pressed by fingers to exert force; the blade can be cleaned or wiped after material taking is finished, then the blade is retracted into the knife shell, and the outer wall of the blade is attached to the aseptic cotton in a sliding mode to be wiped and disinfected in the retracting process.

The application provides a laryngeal squamous cell carcinoma prognosis gene methylation marker and application thereof, and relates to the technical field of clinical medicine. The methylation marker is CORO1C and MAPK11, and a recurrence risk prediction model is constructed based on the two sites. The prediction model is independent of factors such as stage, age and differentiation of patients, proving the independence and universality of the prediction model. Through the RRBS technology, the methylation changes of the genes can be comprehensively analyzed at high resolution, and the prognosis value of the markers in laryngeal squamous cell carcinoma is verified. Compared with traditional single clinical pathological indicators, the methylation marker based on the molecular level provided by the application has high accuracy and sensitivity, and can provide more accurate basis for the recurrence prediction of laryngeal squamous cell carcinoma.

This invention belongs to the field of artificial intelligence technology, specifically a deep learning-based method for early warning and auxiliary diagnosis of animal epidemics in animal husbandry and veterinary medicine. It includes constructing an environmental-physiological feature decoupling model to separate environmental stress from pathological deviations; using graph convolutional neural networks to analyze the dynamic evolution of group social topology and capture individual out-of-group tendencies; employing a spatiotemporal multi-scale feature fusion architecture to integrate and process physiological sequences, posture features, and social relationships; and using a biological attribute mapping layer to reverse-map the neural network state into clinical pathological descriptors. This application achieves environmental interference removal and accurate capture of the epidemic incubation period, provides logically traceable auxiliary diagnostic reports, and significantly improves the certainty of epidemic prevention decisions.

The application discloses a pathological grading method and device, a storage medium and a computer program product, relates to the technical field of medical image processing, and relates to the technical field of medical image processing. The pathological grading method comprises the following steps: acquiring a light microscope image of a kidney biopsy, and detecting the light microscope image by using a preset glomerulus recognition model to obtain a section image of each glomerulus; according to the section image, determining a proportion result of each type of glomerulus in the light microscope image; and according to the proportion result and a preset pathological grading rule, determining a pathological grading result. The application automatically identifies and classifies the glomerulus in the biopsy image, and automatically classifies according to the clinical pathological grading standard, thereby reducing the work load of the clinician in the pathological grading, and improving the accuracy and efficiency of the pathological grading.

The invention belongs to the technical field of antibody preparation, and particularly relates to a rabbit-derived antibody aiming at a human MITF transcription factor, an antibody conjugate and application of the rabbit-derived antibody and the antibody conjugate. The amino acid sequences of CDR1-3 on a light chain variable region of the antibody are respectively shown as SEQ ID NO.3-5, and the amino acid sequences of CDR1-3 on a heavy chain variable region of the antibody are respectively shown as SEQ ID NO.8-10. The antibody strain provided by the invention can specifically recognize and bind the human MITF transcription factor, has good binding affinity and high recognition sensitivity with the MITF antigen, can accurately recognize MITF protein in cells / tissues, has no cross reaction with other antigen components, has excellent anti-interference capability, and has high coincidence degree of a detection result and a real situation; the method is beneficial for improving the specificity, sensitivity and credibility of an immunodetection result, and is suitable for cell tissue samples with abnormal MITF expression, such as clinical pathological diagnosis of malignant melanoma.