417 results about "Disease progression" patented technology

The invention discloses a glioma T cell prediction and prognosis evaluation method based on pathological images, particularly relates to the field of patient prognosis health evaluation, and aims to solve the problems that existing pathological evaluation is difficult to combine with tumor structure heterogeneity and immune infiltration distribution, and the future progress risk of a patient cannot be predicted based on follow-up visit pathological data. A spatial heterogeneity map of a tumor core area and an invasion edge is constructed in a digital pathological section, density gradients of T cells in different areas are calculated to generate a distribution heterogeneity coefficient, interaction processing is performed on the two types of characteristics in combination with historical follow-up visit records, and a time sequence neural network constructed based on a gating structure is combined to obtain a high-efficiency characteristic of the tumor. And outputting disease progress probabilities of a plurality of follow-up visit time points in the future to form a prognosis trajectory prediction curve, calculating a quantitative recurrence risk score according to curve slope change and an immune fluctuation mode, and finally generating an individualized management scheme, thereby realizing quantitative prediction evaluation and risk management of the prognosis trend of the glioma patient.

The invention discloses a hypertensive heart disease early screening system fusing ultrasonic multiple parameters, which comprises the following steps: acquiring ultrasonic image data and physiological index monitoring parameters of a patient at different time points, performing difference comparison on heart structure parameters to generate time sequence change data, and establishing a multi-dimensional data fusion scheme based on the time sequence change data and physiological indexes; according to the fusion scheme, acute risk trigger factors are identified, blood flow abnormal parameters are generated, and emergency threshold comparison is carried out to generate acute risk early warning signals; extracting a myocardial strain degradation rate to determine a chronic degradation acceleration time point, carrying out disease progress stage association to generate a chronic risk layering curve, and establishing a clinical index comparison table; carrying out double-layer correlation on the acute risk early warning signal and the chronic risk layering curve, and carrying out calibration judgment to generate a comprehensive risk judgment sequence; the sequence is decomposed into an acute judgment layer and a chronic judgment layer, a risk threshold value is configured to generate an instant early warning signal, a layered screening report is formed, and technical support is provided for early discovery of the hypertensive heart disease.

The invention discloses a data-driven early-diabetic future diabetes-related disease progress risk prediction model and a construction method thereof, and the method comprises the steps: collecting clinical index data, including age, gender, BMI, WHR, HOMA-IR, HDL-C, TG, SBP, DBP, SCR and ALT, of early-diabetic patients in a training and verification queue; using an unsupervised soft clustering method combining dimension reduction based on UMAP, graph clustering and a Gaussian mixture model to identify the phenotypic heterogeneity of the prediabetes mellitus; obtaining the probability of the individual phenotype characteristics, evaluating the association between the probability and the development risk of the future diabetes related diseases in the early stage of diabetes, and constructing a development risk prediction model of the future diabetes related diseases in the early stage of urine diseases; and performing model optimization and robustness verification in the verification queue. According to the method, the heterogeneity of the prediabetes mellitus can be effectively identified, and accurate risk stratification and personalized prevention are realized.

The invention relates to application of a DDX39B protein inhibitor in preparation of acute leukemia treatment medicines, and belongs to the technical field of biological medicines. In order to solve the problems of single acute leukemia treatment scheme and lack of novel medicines at present, the invention provides application of a DDX39B protein inhibitor in preparation of acute leukemia treatment medicines. Targeted inhibition of DDX39B can significantly hinder proliferation of leukemia cells, promote apoptosis and differentiation of cells, retard the process of cell cycles, delay disease progression in vivo and prolong the lifetime of model animals. On the basis of an RNA (Ribonucleic Acid) binding pocket structure of DDX39B, through high-throughput drug screening, the small molecule compound HMU-4051C is firstly identified as a DDX39B targeted inhibitor to effectively kill leukemia cells, and a new application of the DDX39B protein inhibitor in preparation of acute leukemia treatment drugs is developed.

The invention provides a clinical decision knowledge graph construction method and system, and the method comprises the steps: extracting standardized entities corresponding to diseases, symptoms and diagnosis and treatment elements from medical knowledge data; constructing a clinical concept knowledge graph for representing a medical concept logic relationship and a causal relationship according to the semantic association relationship and the causal dependency relationship among the standardized entities; a diagnosis event, an examination event and a treatment event related to the patient are extracted, link evidences among the events are determined based on the event chain relation among the events, and a clinical event knowledge graph used for representing the disease course evolution process of the patient is constructed according to all the link evidences; and performing knowledge element fusion based on an entity association relationship between the clinical concept knowledge graph and the clinical event knowledge graph, and generating a target knowledge graph for clinical decision analysis. By adopting the scheme of the invention, the cross-map fusion of the static medical concept knowledge and the dynamic disease course event chain relationship can be realized, and the clinical decision knowledge structure with the reasoning ability can be constructed.

The application discloses an exosome small non-coding RNA molecule marker and application thereof, and belongs to the field of tumor liquid biopsy and molecular diagnosis. The exosome small non-coding RNA molecule marker is at least one of tRNA-GlyGCC-5 and sRESE; the nucleotide sequence of the tRNA-GlyGCC-5 is shown as SEQ ID NO:1; and the nucleotide sequence of the sRESE is shown as SEQ ID NO:2. The sRESE in the application is a brand new small non-coding RNA sequence, and is first discovered by the inventor, which is important for the marker of esophageal cancer and the research on disease progression mechanism. The tumor diagnosis kit provided by the application, which comprises the exosome small non-coding RNA molecule marker, is a liquid diagnosis monitoring kit with non-invasiveness, high sensitivity, high specificity and applicability to general survey or screening.

The invention relates to the technical field of medical image processing, and particularly discloses a lesion image generation method for ultrasonic diagnosis, which comprises the following steps of: 1, acquiring initial state data of a lesion; 2, obtaining intervention factor data; 3, performing feature coding on the initial state data and the intervention factor data to obtain an initial feature vector and an intervention feature vector; and 4, inputting the initial feature vector and the intervention feature vector into a pre-trained time sequence generation model, and generating a continuous image sequence of the focus changing along with time. By constructing the time sequence generation model fusing the multi-modal feature coding and the space-time attention mechanism, the continuous image sequence of the focus changing along with time can be accurately generated, the disease progress or treatment response process can be accurately simulated, and a powerful reference basis is provided for clinical prognosis evaluation.

The invention belongs to the technical field of biological medicine, and particularly relates to an extracellular vesicle drug delivery system, a preparation method thereof and application of the extracellular vesicle drug delivery system in treatment of Alzheimer's disease. According to the invention, one or more of stilbene glucoside, emodin, kaempferol, resveratrol and quercetin are used for regulating and controlling the expression of miRNA (micro Ribonucleic Acid) related to the disease progress in the extracellular vesicles, and the result shows that one or more of miR-let-7c-5p, miR-486-5p, miR-132-3p, miR-160b and miR-129-5p is remarkably up-regulated, and one or more of miR-342-3p, miR-16-5p and miR-125b-5p is remarkably down-regulated. The regulated extracellular vesicles can be used for treating Alzheimer's disease, relieving Tau protein abnormal phosphorylation, reducing amyloid protein beta deposition, inhibiting microglial cell activation, protecting neuronal cells and relieving memory deficits.

The invention discloses an early gastric cancer intelligent screening and risk layering method and system based on multi-modal data fusion and deep learning. The method comprises the following steps: acquiring a gastroscope image, a pathological image, a medical image and clinical data of a patient, performing standardization processing and forming unified feature representation; training the multi-branch neural network model group, and generating a virtual queue tag based on each mode; fusing a multi-modal queue division result to generate a patient comprehensive queue label; adaptively selecting a decision path according to the label, and executing feature fusion and analysis of corresponding depth; generating a structured report containing gastric cancer risk levels and individualized monitoring suggestions; and deploying the system and dynamically tracking the change of a patient queue to realize continuous monitoring of disease progression. Cross-modal semantic alignment, uncertainty evaluation and a Markov prediction model are introduced into the system, the screening accuracy, credibility and dynamic risk evaluation capability are improved, and the system is suitable for early screening and personalized management of gastric cancer.

Biomarkers can be assessed for a variety of uses, including screening, detection, diagnosis, prognosis, risk prediction, disease progression, recurrence, selection of treatment, therapy response, to evaluate a subject's health status, whether the subject presents with no evidence of disease, or a benign or malignant condition such as cancer. Compositions (antibodies, polypeptide and polynucleotide markers) and methods are provided herein, which find application in the early detection of cancer, in the early detection of disease relapse and in monitoring therapy response.

This invention relates to the field of medical information management technology, including an AI-based endometriosis management system. The system comprises a thickness dynamic monitoring module, an inflammation level assessment module, a lesion spread analysis module, a periodic lesion tracking module, and a disease progression analysis module. In this invention, AI is used to segment ultrasound image data, enabling dynamic monitoring of lesion areas. Lesion boundaries are extracted, and changes in local inflammatory marker concentrations are calculated, making the screening of abnormal inflammatory areas more accurate and enabling early identification of lesion development. The tracking of periodic lesions, combined with analysis of lesion contour changes and area increases / decreases, enhances the quantitative control of disease progression. The future development trend of lesions, combined with the calculation of periodic inflammatory pathways, makes the prediction of disease evolution more consistent with physiological changes. Multi-dimensional lesion analysis combined with AI intelligent calculation expands the diagnosis and treatment of endometriosis from static assessment to dynamic trend prediction, improving the adaptability of personalized treatment plans.

The application discloses application of a product for detecting metabolite levels in serum in preparation of a preparation for diagnosing systemic lupus erythematosus (SLE) and SLE patients with different organ involvement phenotypes and a reagent. The application first uses specific serum biomarkers to assist in screening SLE, SLE with different disease activity and SLE patients with different organ phenotypes, including only kidney involvement systemic lupus erythematosus (KI), only skin involvement systemic lupus erythematosus (SI), only blood system involvement systemic lupus erythematosus (BI) and multi-system involvement systemic lupus erythematosus (MI), thereby opening up a new way for the diagnosis of the field and helping to more accurately diagnose and evaluate the disease progression of SLE. The blood specimen required is small, easy to carry out and has a good application prospect.

The present application relates to the technical field of medical information processing, in particular to a liver cirrhosis prognosis risk detection system. After each detection of the patient's liver by the system, the image data can quantify the image liver cirrhosis risk from the morphological changes in the liver region. According to the difference between the actual detection data and the prediction data, and the prognosis response non-normality, the disease progression index is determined. The drug adjustment data is used to determine the drug adjustment degree, and then the liver cirrhosis progression index is obtained. The data lag and the liver cirrhosis progression index are quantified to adjust the detection frequency of the system. The present application enables the system to effectively respond to the patient's condition and generate an effective early warning signal to remind medical staff to perform liver detection when necessary.

The invention provides application of dynamic cortex inhibition imbalance in clinical progress prediction of early Parkinson's disease. The key function of bilateral cortex inhibition balance in adjusting the motion compensation ability of the Parkinson's disease in the early stage is clarified. By analyzing the change of the two inhibition pathways, the change of delta SICI ' / LICI' is found to be a sensitive index capable of predicting the disease progress of the patient with the early Parkinson's disease. ROC results show that the delta SICI ' / LICI' has very high discrimination capability in the aspect of predicting disease progression, the area under curve (AUC) is 0.841, the sensitivity is 87.5%, the specificity is 72.73%, and the Eyden index is 0.602. The research of the invention makes deeper understanding of the pathophysiological mechanism of Parkinson's disease, and highlights the importance of cortex inhibition balance in disease evolution. These observations also support the development of more targeted therapeutic interventions to alleviate symptom progression.

A method of treating male hypogonadism in a subject in need thereof includes administering to the subject testicular organoids generated from isolated male progenitor cells. The method can be used to slow or halt the progression of age-related diseases, and increase healthspan and longevity, in hypogonadal males. The method can be used with subjects that have not or are not receiving testosterone replacement therapy. The method does not require that the subject is pre-treated with CG. The testicular organoid includes cells expressing GFRA1, VEGF, VEGFR2, PDGF-Rα, Cyp11A1, 3β-HSD, 17β-HSD, LHCGR, α-SMA, CD11b, MHC CII, CD64, CD95, Sox9, AR, inhibin β-B, and FSHR.

Described herein are methods and compositions related to methylation-based determination and characterization of subjects afflicted with various stages of cancer, including lung cancer and non-small cell lung cancer (NSCLC). Osimertinib is one of the most common precision therapies in non-small cell lung cancer but nearly all patients eventually progress due to somatic mutations and cellular plasticity that bypass the treatment. Defining the cellular plasticity state with epigenetics will help physicians know when to switch therapies.

The invention discloses an overexpression vector of LAMP2B and application thereof, which can effectively delay the disease progress of DMD, and comprises: (a) an AAV vector which is an adeno-associated virus vector and has high muscle tissue targeting and low liver tropism; (b) a CMV (cytomegalovirus) promoter, wherein the CMV promoter is a cytomegalovirus promoter; (c) a coding sequence of an hLAMP2B gene, namely a coding sequence of a human LAMP2B gene, and the coding sequence of the hLAMP2B gene is operably linked to the CMV promoter; wherein the vector is configured to overexpress LAMP2B protein in muscular tissue, and the vector is self-complementary AAV, namely scAAV, the concept is ingenious, the novel AAV vector with high muscle transformation efficiency is utilized to deliver a complete LAMP2B gene (LAMP2B is a gene related to autophagy regulation) into muscle cells of all organs of a mouse (AAV-hLAMP2B for short), and the autophagy regulation function of the mouse is improved. The muscle tissue function of the mouse DMD disease model is comprehensively assisted to recover, and the disease progress of the DMD is delayed.

This invention discloses a method for constructing a gene methylation diagnostic model for differentiated thyroid cancer (PTC). The method includes the following steps: S1, obtaining a test sample; S2, extracting and storing DNA from the test sample; S3, performing methylation analysis; S4, establishing a diagnostic model based on DNA methylation, where the model shows that patients with cg03596178, cg06033721, cg06688989, cg07209244, cg07485775, cg14484681, cg19979108, and cg20943461 are more likely to experience disease progression and are considered risk factors; S5, cross-validating and evaluating the performance to obtain the gene methylation diagnostic model. This model, by detecting and statistically analyzing specific methylation sites, can differentiate patients with recurrent or metastatic characteristics in differentiated thyroid cancer. The relevant model can assist in the clinical diagnosis and follow-up of thyroid patients.

The invention provides a triple therapy medicine composition for treating chronic knee joint injury and a preparation method and an injection product thereof, and belongs to the technical field of medical biology. The invention provides a triple therapy pharmaceutical composition for treating knee joint injury. The pharmaceutical composition consists of the following components in parts by volume: 2-5 parts of platelet-rich plasma, 2-5 parts of mesenchymal stem cells and 2-5 parts of exosomes. The platelet-rich plasma, the mesenchymal stem cells and the exosome promote each other, so that the effects of relieving pain and promoting cartilage repair are superimposed and strengthened, the symptoms and functions are improved, the disease progression is delayed or partially reversed, and a relatively good effect on treating knee joint injury is achieved.

The invention relates to the technical field of medicine, in particular to an ophthalmology clinic system and method simulating a three-level diagnosis and treatment mode and medical device.The system comprises a triage module provided with a first diagnosis model and capable of achieving eye disease screening and early triage based on single-mode image data and clinical data of eyes of a user; the diagnosis module is provided with a data fusion model and a second diagnosis module and can realize accurate recognition and disease progress prediction of complex eye diseases, the decision module is provided with a third diagnosis model and can provide personalized eye disease treatment scheme suggestions for patients, and the patients are propagandized and educated in combination with a large language model, so that the diagnosis efficiency is improved. And the clinical decision-making efficiency and the self-management ability of the patient are improved. Therefore, the problems of limited screening capability, low accuracy, incapability of effectively predicting the development trend of diseases and the like due to the fact that ophthalmology diagnosis mostly depends on single image analysis in related technologies are solved.

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

It is provided an anti-Aβ34 antibody and its use for diagnosing Alzheimer's disease in a patient, comprising obtaining a sample from the patient, detecting the level of Aβ34 in the sample by contacting the sample with the anti-Aβ34 antibody and detecting binding between Aβ34 and the antibody, and diagnosing the patient with Alzheimer's disease when the presence of Aβ34 in the sample is detected, alone or in combination with detecting an amyloid deposition marker such as Aβ42 and determining the ratio of Aβ34 / Aβ42.

The invention belongs to the technical field of disease model construction, and particularly relates to a construction method of a chronic myelogenous leukemia mouse model with conditional human EZH2 overexpression and RUNX1 knockout. According to the invention, a chronic myelogenous leukemia mouse transgenic mouse model with conditional human EZH2 overexpression and RUNX1 knockout is successfully constructed, the model is induced to be converted from a chronic stage to a sudden change stage, and particularly, the model is a transgenic mouse model which is positive in Lyz2-CreERT2 / EZH2 / RUNX1 and carries BCR-ABL and SCL-tTA. It is proved that a human EZH2 conditional overexpression and RUNX1 knockout chronic myelogenous leukemia mouse transgenic mouse model has feasibility and importance for research on conversion from CML CP to BC samples, and a molecular mechanism for conversion from chronic myelogenous leukemia to a sudden change stage is revealed for research. And a new animal model and a new research idea are provided for understanding of disease progression and development of a new treatment strategy.

Provided is a method of digital therapeutics for modulating one or more Parkinson's disease-related markers, symptoms, or disease progression in an individual, on a standalone basis or by combining them with medicinal agents, particularly dopaminergic agents. Also provided are corresponding systems for implementing, delivering, and adapting such therapeutic interventions.

The application discloses application of a benzimidazolone compound in preparation of a medicine for treating intervertebral disc degeneration. 11 H 14 N4O2, and has a CAS number of 881591-33-9. The application provides an innovative treatment strategy capable of intervening in an early stage of the disease and influencing disease progression from a cause level by targeting activation of SOD1 activity which is a newly found pathogenic link of intervertebral disc degeneration.

The invention provides a disease progress probability prediction method and device based on a Monte Carlo-Markov chain, and relates to the technical field of disease progress prediction. The method comprises the following steps: acquiring multi-modal historical follow-up visit data of a patient to be tested, and constructing a state space with transfer constraint; extracting and checking time series data according to the state space; performing Monte Carlo sampling on the time sequence data, establishing a non-homogeneous transfer model and forming an individualized non-homogeneous transfer kernel set; completing path interpolation and probability filling of missing fragments based on the transfer kernel set to obtain continuous complete state sequence data; a Markov chain Monte Carlo algorithm is used for parameter updating, an individualized transfer model subjected to posteriori updating is obtained, a calibration probability result is obtained, and dynamic prediction and uncertainty evaluation of individualized disease progression are achieved. According to the method, the problems of inaccurate state determination, single transfer modeling, incomplete observation and insufficient prediction calibration in the prior art are solved.

The present application relates to the field of medical immunology technology, and more particularly to biomarkers and their application in the assessment of liver damage in end-stage liver disease. In the present application, the proportion of CCR7+CD8+ T cells in CD8+ T cells is used as a marker for the assessment of liver damage in end-stage liver disease, which can reflect the liver immune status of end-stage liver disease, especially the assessment of the degree of liver damage, has high specificity and sensitivity, and provides a scientific basis for the development of clinical individualized treatment plan, and has good practical application value. At the same time, while reflecting the liver damage process of the patient, it can also reflect the immune status of the patient's liver, and can provide certain theoretical basis for slowing down the disease progression of subsequent biliary atresia and other end-stage liver disease patients with liver damage.

The present disclosure describes systems configured to recognize indicators of a medical condition within a diagnostic image and predict the progression of the medical condition based on the recognized indicators. The systems can include neural networks trained to extract disease features from diagnostic images and neural networks configured to model the progression of such features at future time points selectable by a user. Modeling the progression may involve factoring in various treatment options and patient-specific information. The predicted outcomes can be displayed on a user interface customized to specific representations of the predicted outcomes generated by one or more of the underlying neural networks. Representations of the predicted outcomes include synthesized future images, probabilities of clinical outcomes, and / or descriptors of disease features that may be likely to develop over time.

The present invention relates to use of expression vectors and other compounds in methods to disrupt the Linker of Nucleoskeleton and Cytoskeleton (LINC) complex, uncoupling the nucleus from its linkage to the cytoskeleton, resulting in amelioration of diseases caused by one or more Lmna mutations, so-called laminopathies. More particularly, the invention relates to the expression of dominant negative SUN domain protein and / or dominant negative KASH domain protein to disrupt, for example, the LINC complex in cardiomyocytes for suppressing disease progression in dilated cardiomyopathy (DCM).

A method for treatment of one or more toxin(s) in a subject, that includes administering to the subject a therapeutically effective amount of a serine protease that enzymatically cleaves certain toxins. Also, a method for preventing disease progression in a subject infected by Clostridioides difficile (CD), diarrhea, or infectious colitis, which includes administering to the subject a therapeutically effective amount of the protease. The protease retains its activity up to 65° C. Treatment with the protease in mice infected with CD conferred a 10-fold survival benefit compared to mice infected with CD and untreated.