74 results about "Disease prognosis" patented technology

The invention discloses a disease prognosis evaluation and dynamic prediction method based on time sequence analysis, which comprises the following steps: acquiring disease prognosis medical data of a patient to construct a medical data time sequence set, extracting medical data time sequence characteristics and constructing a prognosis evaluation model, updating the prognosis evaluation model, and performing dynamic prediction. Inputting the to-be-evaluated patient disease prognosis medical data into the updated prognosis evaluation model to obtain a disease prognosis evaluation result, constructing a prognosis prediction model, inputting the to-be-evaluated patient disease prognosis medical data into the prognosis prediction model to obtain a disease prognosis prediction result, and updating the disease prognosis prediction result. And generating evaluation interpretation, prediction interpretation, a disease prognosis evaluation chart and a disease prediction trend chart. According to the method, the efficiency and accuracy of disease prognosis evaluation and prediction can be improved, timely and effective decision support is provided for clinic, the requirements of personalized medical treatment are met, and more accurate treatment schemes and rehabilitation guidance are provided for patients.

The invention is applicable to the technical field of biology, and provides edible lactobacillus for relieving pulmonary fibrosis and application thereof, the edible lactobacillus for relieving pulmonary fibrosis provided by the embodiment of the invention is named as lactobacillus plantarum ZYHL3, is preserved in China Center for Type Culture Collection (CCTCC), has a preservation number of CCTCC No: M 2025127, has a preservation number of CCTCC No: M 2025127, and has a preservation number of CCTCC No: M 2025127, a preservation number of CCTCC No: M 2025127, a preservation number of CCTCC No: M 2025127, a preservation number of CCTCC No: M 2025127, and a preservation number of CCTCC No: M 2025127. Lung inflammation and pulmonary fibrosis of mice can be remarkably relieved and disease prognosis can be improved by adjusting intestinal flora diversity and metabolic profiles, so that a new thought can be provided for preventing and treating pulmonary fibrosis diseases such as idiopathic pulmonary fibrosis and the like.

The invention discloses an antibody combination and a system for detecting leukemia stem cells. The antibody combination comprises a CD7 antibody, a CD371 antibody, a CD34 antibody, a CD45RA antibody, a CD123 antibody, a CD133 antibody, a CD38 antibody, a CD45 antibody, a CD90 antibody, a CD33 antibody and a CD19 antibody. The antibody combination provided by the invention can accurately identify target cells and distinguish normal HSCs and pluripotent progenitor cells in a CD34 + CD38-cell population, and the specific antibody has abnormal expression or expression intensity change, so that leukemia stem cells can be rapidly, simply and conveniently detected with high accuracy, high sensitivity and high specificity, the sensitivity of MRD evaluation is favorably improved, and the kit has a good application prospect. Therefore, signs of disease recurrence can be found earlier, more information about disease prognosis can be provided, and doctors and patients can be helped to better understand disease prognosis and possible results.

The invention belongs to the technical field of gestational diabetes diagnosis marker screening, and particularly relates to a biomarker for predicting or diagnosing gestational diabetes and application of the biomarker. The invention discloses a biomarker for predicting or diagnosing gestational diabetes mellitus, the biomarker is selected from one or more of hypoxanthine, cortisol, dihydrotachysterol and cycloleucine, and more preferably, the biomarker comprises hypoxanthine, cortisol, dihydrotachysterol, (3R, 4R)-D-ketolactone, creatine, cycloleucine and L-hydroorotic acid. The seven biomarkers disclosed by the invention can predict or diagnose gestational diabetes mellitus independently or jointly through plasma detection at the early pregnancy stage, effectively improve the early diagnosis rate of gestational diabetes mellitus and improve disease prognosis, and have clinical use and popularization values.

A disease prognosis model to may be trained to determine the clinical outcome of a disease based on longitudinal data including a health record for each timepoint in a sequence of timepoints. The disease prognosis model may include a recurrent neural network trained to extract, from each health record, a feature set representative of local dependencies present within the health record. The disease prognosis model may include a spacetime attention trained to determine an importance of each feature in the feature set at each timepoint in the sequence of timepoints. The disease prognosis model may include a feedforward neural network trained to determine, based on the importance of each feature in the feature set at each time point in the sequence of timepoints, the clinical outcome of the disease. The trained disease prognosis model may be applied to determine the clinical outcome of the disease for one or more patients.

The invention relates to application of a combination of FGGY, DSG1 and CUSTOS proteins in prognosis of pancreatic neuroendocrine tumors, and belongs to the field of disease prognosis, the abundance of the combination of FGGY, DSG1 and CUSTOS proteins can be firstly found to be used for judging postoperative recurrence and metastasis risks of PNETs patients, and prognosis in a disease-free survival (DFS) curve of a high-risk group is remarkably poorer than prognosis in a low-risk group; 0.0001) of the method.

The invention discloses a chronic disease prognosis method, a chronic disease prognosis model, a chronic disease prognosis device, computer equipment and a storage medium. The chronic disease prognosis method comprises the following steps: acquiring a medical image and medical data of a to-be-prognosed person; in the medical image, deep learning features and radiomics features are extracted; determining shared features and unique features of the deep learning features and the radiomics features in the deep learning features and the radiomics features; performing fusion processing based on the shared features and the unique features to obtain target fusion features; based on the target fusion features and the medical data, constructing a multi-omics graph; and performing prognosis treatment based on the multiple omics graphs to obtain a prognosis result of the to-be-prognosed person. Through the target fusion features and data of different modalities such as medical data, the constructed multi-omics graph can enhance the modeling ability of the model for complex pathological information on the basis of improving expression complementarity among different modalities, so that the accuracy and robustness of a prognosis prediction result are remarkably improved.

The invention discloses an application of NF-kappa B and IRF4 in prognosis evaluation of diffuse large B-cell lymphoma, relates to the technical field of disease prognosis and molecular biology, provides a marker for prognosis evaluation of diffuse large B-cell lymphoma, and relates to the application of NF-kappa B and IRF4 in prognosis evaluation of diffuse large B-cell lymphoma. The invention relates to application of a substance for detecting the marker in claim 1 in preparation of a product for prognosis evaluation of diffuse large B-cell lymphoma, and provides a medicine for treating diffuse large B-cell lymphoma. By detecting the marker, the malignancy degree of the DLBCL can be more accurately analyzed, a reliable prognosis evaluation basis is provided for clinicians, and accurate prognosis evaluation is carried out; through targeting NF-kappa B and MUM1 dual-blocking therapeutic drugs, the prognosis of patients is improved, the life quality and the survival rate of the patients are improved, and novel therapeutic drugs are developed; a new breakthrough is brought to diagnosis, treatment and prognosis evaluation of the DLBCL, and potential clinical application value is achieved.

Lung cancer remains the leading cause of cancer-related deaths in the world. Despite the fact that introduction of immune checkpoint inhibitors (ICIs) led to a major advancement in lung cancer treatment, disease prognosis continues to remain low and a significant proportion of patients do not respond to such therapies. Cancer vaccines could potentially provide a complementary approach to boost antitumor immunity and act synergistically with ICIs. Novel tumor antigens shared by a large proportion of lung tumor cells are described herein. Several of the tumor antigens described herein derive from aberrantly expressed unmutated genomic sequences, such as intronic and intergenic sequences, which are not expressed in normal tissues. Nucleic acids, compositions, cells and vaccines derived from these tumor antigens are described. The use of the tumor antigens, nucleic acids, compositions, cells and vaccines for the treatment of lung cancer is also described.

Provided relates to the field of cytometry, specifically to flow cytometric methods and kits for improved diagnosis, prognosis and monitoring of tumors and other lesions involving immune cell infiltration. Further provided are embodiments of the subject matter which relate to compositions and methods providing high resolution quantitative means for immunophenotyping and immune modeling, and for identification of disease prognostic and therapy predictive biomarkers.

The utility model discloses a portable traditional Chinese medicine olfactory diagnosis instrument, which comprises a sample collection device, a gas enrichment device, a mass spectrometer, a data processor and a display, the mass spectrometer is sequentially connected with the data processor and the display, the gas enrichment device is test paper with an enrichment reagent encapsulating nano-metal particles, the enrichment efficiency is effectively improved, and the detection accuracy is improved. Therefore, the detection sensitivity and accuracy of the mass spectrometer are improved; the device is provided with the ion trap mass spectrometer, has the characteristics of high detection speed, high resolution and high sensitivity, and has performance advantages in qualitative and trace analysis aspects; and due to small volume and strong portability, the device can be conveniently integrated into portable or field detection equipment, can be used for auxiliary diagnosis of heart failure and asthma, and also has great clinical application value in the aspects of disease evolution and disease prognosis evaluation.

The present application relates to a kind of D type personality prediction method and system based on millimeter wave radar, method includes: when target patient is diagnosed with coronary heart disease, the human heart rate variability HRV data of target patient is collected using millimeter wave radar, short-term frequency domain analysis is carried out to HRV data, and the frequency domain index parameter of target patient is obtained;Whether target patient is D type personality according to preset D type personality prediction algorithm and frequency domain index parameter is predicted;If target patient is D type personality, then target patient is carried out according to D type personality coronary heart disease prognosis.The non-contact HRV data acquisition of coronary heart disease patient is realized using millimeter wave radar, and after short-term frequency domain analysis is carried out to HRV data, the frequency domain index parameter for D type personality prediction is obtained, so as to realize the prediction of D type personality.Compared with the existing scale, the accuracy is higher, and the disease prognosis of D type personality coronary heart disease patient can be effectively improved.

The present invention relates to exosome-derived biomarkers for diagnosing arteriosclerosis. The biomarkers of the present invention exhibit specific changes in expression associated with arteriosclerosis, particularly during the early stages of the disease, and thus are highly effective for early diagnosis and prognosis of arteriosclerosis. Particularly, the 3D arteriosclerosis model of the present invention closely mimics the arterial condition of actual patients, allowing for the combined use of the biomarkers and the model to enable early diagnosis, personalized treatment development, and precise monitoring of disease prognosis.

The invention belongs to the technical field of biology, and relates to a primer and a kit for detecting LOX gene polymorphism and application of the primer and the kit, and application of the primer in preparation of a reagent for detecting LOX gene G473A polymorphism or predicting epilepsy susceptibility, and the sequence of the primer is SEQ ID NO.61-62. The invention also relates to application of the primer in preparation of a reagent for detecting LOX gene G473A polymorphism or predicting epilepsy susceptibility. The LOX gene G473A site polymorphism method provided by the invention improves the accuracy of LOX gene G473A typing, is suitable for rapid detection of LOX gene polymorphism, and compared with the prior art, the method provided by the invention can rapidly and accurately carry out DNA sequence analysis, is convenient to construct a standardized operation process, has the characteristics of high throughput and low cost, is extremely simple and convenient to operate, needs a small amount of samples, and is suitable for large-scale popularization and application. The method is beneficial to early screening of LOX gene G473A site mutation related diseases, prevention and judgment of disease prognosis conditions.

The composition for predicting Alzheimer's disease prognosis comprising a detection reagent that specifically binds to any one or more genes selected from the group consisting of Nos1 (Nitric oxide synthase 1), Aph1B (Anterior pharynx defective 1 homolog B), Ryr3 (Ryanodine receptor 3), Atf6 (Activating transcriptional factor 6), Ip3r (Inositol trisphosphate receptor), Nep (Neprilysin), Cdk5 (cyclin dependent kinase 5) and Abad (Amyloid beta-binding alcohol dehydrogenase), a kit, and a method for providing information on Alzheimer's disease prognosis of the present invention can rapidly and accurately predict the prognosis of Alzheimer's disease, and can determine the prognosis for a drug by comparing the expression level before and after drug administration, so that an appropriate treatment direction can be determined according to the predicted prognosis. Therefore, they have an effect that can greatly contribute to the reduction of mortality due to Alzheimer's disease.

The invention discloses application of ANGPTL3 in prognosis of hepatitis B related chronic-acute liver failure, and relates to the technical field of disease prognosis and biological medicine. The invention discloses application of ANGPTL3 as a biomarker in preparation of a product for diagnosis and / or disease course diagnosis and / or prognosis evaluation of hepatitis B related chronic plus acute liver failure. The invention has the following beneficial effects: ANGPTL3 can be used as a biomarker for prognosis of hepatitis B-related chronic-acute liver failure, blood sample levels of patients in different groups of HBV-ACLF and dynamic ANGPTL3 levels are absolutely quantified by ELISA technology, and hepatitis B-related chronic-acute liver failure phenomena and disease courses can be timely found and detected with other related indexes; therefore, the ANGPTL3 is utilized to provide scientific guidance for management and decision-making of hepatitis B related chronic plus acute liver failure.

The invention relates to application of a SelK gene or an expression product thereof as a biomarker for prognosis judgment of glioblastoma diseases. The invention discloses application of a SelK gene or an expression product of the SelK gene to preparation of a product for performing glioblastoma typing diagnosis, therapeutic schedule selection and / or prognosis evaluation by detecting the level of the SelK gene or the expression product of the SelK gene in a tumor sample obtained from an object. The research shows that the high-expression SelK gene and the protein expression product thereof are both in negative correlation with poor prognosis of glioblastoma, and survival analysis also proves that the overall prognosis of patients with low SelK expression is obviously superior to that of patients with high SelK expression, so that the SelK gene and the expression product thereof can be used as molecular markers for prognosis evaluation and treatment effect. The invention also provides an application of the targeted SKP2 / beta-TrCP1 / CDK4 axis signal channel in preparation, evaluation or screening of drugs for treating glioblastoma.

The invention belongs to the technical field of pathological science, and discloses application of TLSs in preparation of a product for prognosis evaluation of complex vascular malformation. The product realizes prognosis evaluation based on parameters of maturity, distribution and quantity of TLSs in a focus of complex vascular malformation; when the number of the TLSs in the parameter is larger and the maturity is higher, the associated information of the complex vascular malformation that the disease course is longer and the recurrence possibility is high is correspondingly output. The invention provides a specific evaluation standard of the TLSs in complex vascular malformation, and a prediction application value for prompting disease prognosis recurrence of the TLSs. The TLSs can be used as a pathological index for detecting recurrence prognosis by FAVA and PHOST, and the risk of postoperative recurrence of vascular malformation can be predicted by detecting the distribution, the number and the maturity of the TLSs. For pathologists with rich experiences, the maturity of TLSs can be evaluated according to HE slices, and the method has good sensitivity and specificity.

The invention particularly relates to a primer probe and a kit for detecting the copy number of a GJB2 gene of a non-syndromic deafness patient by using microdroplet digital PCR (Polymerase Chain Reaction), and application of the primer probe and the kit. The primer probe combination provided by the invention comprises a target gene detection primer pair, a target gene detection probe, an upstream primer of a reference gene, a downstream primer of the reference gene and a probe of the reference gene, and the sequence information of the primer probe combination is shown as SEQ ID NO.6-11; the invention also provides a kit for detecting the copy number of the GJB2 gene of a non-syndromic deafness patient. When the primer probe combination and the kit provided by the invention are applied to detection of copy number variation of the GJB2 gene of a patient with non-syndromic deafness, the accuracy of hereditary deafness detection is remarkably improved; whether GJB2 gene expression is abnormal or not is judged in an auxiliary mode according to the judgment result, disease prognosis is evaluated, potential treatment drugs are screened, genetic counseling services are provided or personalized medical schemes are formulated, and the method has wide application prospects.

A disease prognosis model is provided that can be trained to determine a clinical outcome of a disease based on longitudinal data including health records for each point in time in a sequence of points in time. The disease prognosis model may include a recurrent neural network trained to extract, from each health record, a set of features representing local dependencies present within the health record. The disease prognosis model may include spatio-temporal attention trained to determine an importance of each feature in the set of features at each point in time in the sequence of points in time. The disease prognosis model may include a feed-forward neural network trained to determine the clinical outcome of the disease based on the importance of each feature in the set of features at each point in time in the sequence of points in time. A trained disease prognosis model may be applied to determine the clinical outcome of the disease for one or more patients.

The invention provides application of a TTR tetramer in prognosis of a heart failure disease. The invention finds that serum TTR tetramer level reduction and NT-proBNP increase are possibly related to adverse clinical outcome events of heart failure patients, such as total cause death and cardiovascular death; after adjusting potential confounding factors, low TTR levels relate to all-cause death and an increase in all-cause rehospitalization risk. Therefore, the serum TTR tetramer and NT-proBNP can be used as potential biomarkers for prognosis of the heart failure disease and independent predictive factors for occurrence of adverse events. A good prediction index is provided for evaluating and improving prognosis of the heart failure disease, so that the survival rate of a patient is increased, the patient is fully benefited, and the application has huge potential and value in clinical application.

The invention relates to the technical field of disease prognosis and molecular biology, in particular to application of immune-related polygenes in prognosis evaluation of diffuse large B-cell lymphoma. The biomarkers are DES, CHIT1, FGF14, CCR4, CTLA4 and VGF (Vertical Growth Factor). According to the method, core genes related to immunity are screened on the basis of diffuse large B cell lymphoma transcriptome data such as GEO and TCGA, and a multi-gene immune model: RS = (-0.0306) * DES + (-0.4683) * CHIT1 + (1.1418) * FGF14 + (0.1132) * CCR4 + (-0.07) * CTLA4 + (-0.3463) * VGF is constructed; according to the immune polygene immune model, diffuse large B-cell lymphoma patients can be divided into high and low RS groups, the survival prognosis of high-RS people is proved to be obviously lower than that of low-RS people, and the survival prognosis can be used as an independent prognosis index.

Provided are methods and kits for early determination of disease outcome and prognosis of Multiple Sclerosis (MS) patients and for adjustment of suitable treatment.

The invention relates to a D-type personality prediction method and system based on millimeter wave radar, and the method comprises the steps: collecting the human body heart rate variability HRV data of a target patient through the millimeter wave radar when the target patient is diagnosed with the coronary heart disease, and carrying out the short-time frequency domain analysis of the HRV data, and obtaining the frequency domain index parameters of the target patient; predicting whether the target patient is D-type personality according to a preset D-type personality prediction algorithm and the frequency domain index parameters; and if the target patient has the D-type personality, performing coronary heart disease prognosis on the target patient according to the D-type personality. According to the method, non-contact HRV data acquisition of a coronary heart disease patient is realized by using a millimeter wave radar, and after short-time frequency domain analysis is performed on the HRV data, frequency domain index parameters for D-type personality prediction are obtained, so that D-type personality prediction is realized. Compared with the existing scale evaluation, the accuracy is higher, and the disease prognosis of the coronary heart disease patient with the D-type personality can be effectively improved.

The invention belongs to the technical field of disease prognosis and molecular biology, and particularly relates to a prognosis analysis marker and a prognosis analysis system for a diffuse large B-cell lymphoma patient and application of the prognosis analysis marker and the prognosis analysis system. According to the invention, a prognosis analysis system based on key amino acids and clinical related indexes is established in DLBCL patients for the first time. The progression-free lifetime of the DLBCL patient can be effectively predicted, and the progression-free lifetime is closely related to clinical characteristics of the patient. Furthermore, in key amino acids related to prognosis, tryptophan and glutamine are key molecules, which proves that tryptophan and glutamine have good prognosis value in DLBCL patients. Meanwhile, according to the technical scheme, the prognosis model is imported into the autonomous AI system for operation, autonomous operation, autonomous detection and autonomous adjustment are achieved, and efficiency is improved. A new DLBCL patient prognosis evaluation strategy is provided, a theoretical basis is laid for individualized management of DLBCL patients, and therefore the method has good practical application value.

The present invention is applicable to the field of biomedical technology and provides a diagnostic kit for unstable carotid atherosclerotic plaques. The present invention aims to collect urine samples from patients with carotid atherosclerotic plaques, perform proteomic mass spectrometry sequencing, and conduct comprehensive analysis in combination with transcriptomic and proteomic data of tissue samples. In this process, we successfully discovered two reliable diagnostic biomarkers: ADAMDEC1 and IQGAP2. These two biomarkers can not only be used to prepare diagnostic kits to achieve accurate diagnosis of unstable carotid atherosclerotic plaques, but also provide strong support for the judgment of disease prognosis. In addition, the discovery of the present invention has also opened up a new method for the diagnosis of carotid plaque stability and provided a more reliable basis for clinical prognosis evaluation and the identification of new therapeutic targets.

Electronic devices and methods are provided for disease prognosis using a neural network model and based on demographic information and anatomical images. An electronic device receives demographic information associated with a person. A patient associated with the anatomical region of the person is determined based on application of the first neural network model on the demographic information. A second neural network model is selected from a set of neural network models based on the determined patient. A second neural network model is applied on the received first set of images associated with the anatomical region of the person to determine a first disease severity corresponding to the determined patient. The second neural network model is different from the first neural network model. A display device associated with the electronic device is controlled to present information about the determined severity of the first disease and the determined patient.

The application belongs to the technical field of disease prognosis, and particularly relates to a marker for predicting the prognosis risk of an acute coronary syndrome patient and a system for predicting the prognosis risk. The application provides a marker for predicting the prognosis risk of an acute coronary syndrome patient, which comprises a biomarker and a chronic kidney disease index; the biomarker comprises NT-proBNP and RBP4; and the chronic kidney disease index is an eGFR index. The application also provides a system for predicting the prognosis risk of an acute coronary syndrome patient, which comprises a protein detection module, a chronic kidney disease index detection module, a prognosis score determination module and a prognosis risk evaluation module. The system for predicting the prognosis risk of the application effectively solves the technical problems that the existing GRACE score prognosis prediction method has low prediction accuracy in the East Asian population and has limited prediction value for long-term cardiovascular adverse event risks of more than 1 year.

This invention relates to the field of image analysis technology, specifically to a method for prognostic assessment of breast and thyroid diseases combined with AI analysis of pathological images. The method includes the following steps: acquiring pathological slide images and performing grayscale statistics to generate tissue coordinate data; calculating the edge gradient and contour features of pixel neighborhoods; quantifying the morphological aberration features of cell nuclei and constructing a nuclear population potential field; analyzing the potential difference and gradient inversion regions; correcting directional consistency based on the gradient magnitude distribution direction; and finally generating a disease prognostic assessment result. In this invention, stable tissues are screened and coordinate data is generated through grayscale statistics and threshold segmentation of pathological slide images; edge gradients are calculated and closed contours are extracted; cell nucleus axis ratios and contour roughness are quantified; a regular grid is established and distance attenuation is introduced to form a nuclear population potential field; and the potential difference and gradient inversion regions are statistically analyzed and directional consistency is corrected, thereby improving the stability and consistency of the prognostic assessment.

The application relates to the technical field of disease prognosis risk early warning, and particularly discloses a disease prognosis risk early warning method and system based on big data, which comprises the following steps: obtaining all reference prediction patients of a current patient based on all types of health data of the current patient; obtaining the evaluation and assignment of each type of health data of the current patient based on each type of health data of the current patient and an evaluation and assignment model of each type of health data; obtaining a disease early warning reference graph and a disease early warning comparison graph of the current patient based on the evaluation and assignment of all types of health data of all reference prediction patients of the current patient; and obtaining a disease prognosis risk estimation value of the current patient based on the disease early warning reference graph and the disease early warning comparison graph of the current patient, and then obtaining a disease prognosis risk early warning result of the current patient. The application realizes accurate quantification of the degree of future development and recovery risk of the disease of the current patient, improves the accuracy and reliability of disease early warning, and ensures effective use of resources.