70 results about "Patient survival" patented technology

The invention belongs to the technical field of data processing, and particularly discloses a method and system for predicting the lifetime of a non-small cell lung cancer patient after radiotherapy, and the method comprises the steps: collecting multi-time-point imaging and hematology follow-up visit data of the patient after radiotherapy, building a longitudinal follow-up visit sequence, and mapping the longitudinal follow-up visit sequence to a unified time axis; determining a bimodal first-time significant improvement point through preset rule judgment, and constructing and standardizing an asynchronous index according to the bimodal first-time significant improvement point; then inputting a feature vector at a follow-up time point, encoding a bimodal sequence through a Transform time sequence encoder, and generating joint representation through cross-modal interaction; and finally, inputting the standardized asynchronous index, the joint representation and the clinical and radiotherapy characteristics into a DeepHit model, and outputting a patient survival distribution prediction result. According to the method, bimodal indexes can be accurately captured, time dislocation is improved, prediction accuracy and individualization degree are improved, and a reliable basis is provided for clinical prognosis evaluation.

A multimodal deep learning framework which is used to determine the likelihood of a particular treatment method effectively treating a patient with ovarian / kidney cancer with the goal of increasing patient survival. The framework takes into account not only large histopathology images (whole slide images), but also clinical variables to increase the scope of the data. The results demonstrate that the proposed models achieve high prediction accuracy and interpretability and can also be transferred to other cancer datasets without significant loss of performance. One of the key innovations here is the combination of pathology and clinical variables in a deep learning model to provide recommendations in therapy areas with limited information.

The invention provides a glioblastoma patient lifetime prediction method and system, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: obtaining and coding a digital full-slice image of a patient; screening high-information-content image blocks based on information entropy; executing a dendritic calculation rule and a structural plasticity rule through internal learnable dendritic neurons by using a dendritic morphological neural calculation model, and extracting global and local features from the image blocks; fusing the features through feature fusion neurons to obtain depth features; and finally, predicting the total lifetime through the output neurons. By simulating hierarchical calculation and plasticity of neuron dendrites, a double-level feature learning model capable of integrating global context and local details of a full-slice image at the same time is constructed, and the technical problems that an existing prediction method is insufficient in feature integration and limited in model calculation structure are effectively solved. And more comprehensive and accurate prediction of the lifetime of the glioblastoma patient is realized.

The invention discloses an application of a PLSCR1 protein in preparation of a biomarker for evaluating early warning of sepsis. The invention determines and verifies that the PLSCR1 is obviously increased in sepsis patients for the first time. According to the invention, the PLSCR1 is used as a novel biomarker for early warning and prognosis evaluation of sepsis, so that the clinical early recognition and prognosis capability is effectively improved. The PLSCR1 shows an independent prediction value, detection of the PLSCR1 level of a suspected or diagnosed sepsis patient can provide a more accurate and more comprehensive basis for clinical decision making, and improvement of the survival outcome of the patient and optimization of the treatment effect are facilitated.

The invention provides a method and device for predicting the lifetime of a tumor patient, computing equipment and a storage medium. The method comprises the following steps: acquiring multi-dimensional medical data related to the condition of a tumor patient, wherein the multi-dimensional medical data at least comprises data of at least two dimensions; determining a similar dataset of the multi-dimensional medical data from a reference dataset, the reference dataset including reference multi-dimensional medical data and corresponding lifetime of each of a plurality of reference patients having reached the end of life at different time points; and obtaining a prediction result of the lifetime of the tumor patient based on the multi-dimensional medical data and the similar data set by using a fine-tuned large language model, the prediction result indicating the lifetime of the tumor patient and an explanatory text of information on which the prediction result is based.

The invention provides an illness state tracking and early warning system based on a knowledge database, and the system comprises a data module which is responsible for collecting and integrating clinical knowledge and patient data information in the cardiovascular field; the survival rate prediction analysis module is used for extracting pathological features from the patient model, performing survival rate prediction analysis by using a deep learning algorithm and a neural network model, and displaying a prediction result to a physician in a visual report form; and the illness state auxiliary tracking, studying and judging module monitors the illness state change of the patient in real time, analyzes the illness state development trend and risk factors in combination with a knowledge graph and a patient model, and provides treatment scheme recommendation and early warning notification functions. Through the beneficial effects of integrating medical resources, improving data security, accurately predicting the survival rate of a patient, monitoring and early warning in real time, widely applying a knowledge graph, being friendly to user interaction, feedback circulation and continuous optimization and the like, powerful support is provided for prevention, diagnosis and treatment of cardiovascular diseases.

The present invention relates to a biomarker for predicting immunotherapeutic responsiveness based on spatial transcriptome analysis and uses thereof and, in particular, to: a marker composition for predicting the responsiveness of cancer patients to immunotherapy; a composition for predicting the responsiveness of cancer patients to immunotherapy; a kit for predicting the responsiveness of cancer patients to immunotherapy, comprising the composition; a method for providing information for predicting the responsiveness of cancer patients to immunotherapy; and a method for providing information for predicting the survival prognosis of cancer patients. The biomarker for predicting immunotherapeutic responsiveness, according to the present invention, was discovered by applying spatial transcriptome technology and analyzing cell group-specific gene expression values according to location information of cells in tissue sections, and can more precisely and accurately predict the responsiveness of cancer patients to immunotherapy and the survival prognosis of patients, thus enabling suitable treatments for patient groups, which may result in improved therapeutic effects and a reduction in pain and costs for patients.

The application discloses a cancer survival prediction method and device based on whole-slice pathological images, and relates to the technical field of medical artificial intelligence and digital pathological image analysis. The method comprises the following steps: acquiring whole-slice pathological images and patient survival information, and performing pretreatment; performing cell detection on the whole-slice pathological images to obtain cell structure data; dividing the whole-slice pathological images into a plurality of image regions, and determining regions of interest; screening the cell structure data in each region of interest to construct a cell subset; sorting the cell structure data according to the distribution relationship of cells in space to construct a cell structure sequence; constructing a cell structure modeling model, performing feature modeling on the cell structure sequence, and obtaining tissue structure features capable of representing the microstructure of tumor tissues; and inputting the tissue structure features into a survival prediction model to predict the survival risk of the patient. The application can improve the accuracy and stability of cancer survival prediction.

The application belongs to the technical field of tumor immunotherapy, and particularly relates to a composition for enhancing tumor immunogenicity and application thereof. The composition for enhancing tumor immunogenicity comprises, in terms of mass fraction, 10-15 parts of radix stephaniae tetrandrae, 10-20 parts of radix astragali, 3-9 parts of glycyrrhiza uralensis, and 3-12 parts of atractylodes macrocephala. The composition for enhancing tumor immunogenicity provided by the application is a natural traditional Chinese medicine prescription, has good biocompatibility, low toxicity and side effects, and is suitable for long-term treatment. The composition enhances immunogenicity by up-regulating MHC-I expression of tumor cells, activates CD8 + T cell-mediated anti-tumor effect, realizes autologous immunotherapy, and can avoid related side effects of exogenous immunotherapy. The composition can also be used in combination with a PD-1 blocker to synergistically improve anti-tumor efficacy and prolong patient survival.

The present invention belongs to the technical field of medical diagnosis. Provided are a marker and a kit for the clinical diagnosis of prostate cancer and the use. By means of integrating the expression or variation of multiple genes, the marker can more comprehensively reflect the biological characteristics of prostate cancer, thereby improving the diagnostic accuracy. Since prostate cancer may present no obvious symptoms in the early stage, such a highly sensitive marker can assist physicians in detecting prostate cancer in the early stage, and can thus enhance the therapeutic efficacy and patient survival rate. The kit and system based on the marker enable the diagnosis or prediction of prostate cancer in a subject by means of acquiring detection information of the marker and combining the information with a preset threshold, thereby allowing for the efficient processing of a large volume of data and providing accurate diagnostic results. Therefore, the present invention not only contributes to the diagnosis and treatment of prostate cancer, but may also offer new insights into the pathogenesis and prevention of prostate cancer.

The invention discloses a multivariate data fusion lung cancer patient survival prediction model, and relates to the technical field of cancer survival prediction, and the key points of the technical scheme are that valuable prediction factors are screened based on multivariate and large-scale real world clinical diagnosis and treatment data, traditional machine learning, deep learning and a generative large language model (LLM) are used, and the lung cancer patient survival prediction model is obtained. According to the method, multivariate data such as traditional Chinese medicine syndromes and western medicine indexes are combined, a static survival prediction model is constructed, prediction factors are screened, model performance is compared, prediction efficiency is improved, and the more effective prognosis prediction method for the lung cancer patient is provided.

The MYC and NMYC transcription factors (TFs) play a key role in cell proliferation and are overexpressed in most cancer cells. However, in normal cells their overexpression triggers safeguard mechanisms promoting cell death and cellular senescence, which are bypassed in cancer cells. Here, the inventors reveal that in normal cells MYC binds to the Inositol 1,4,5-Trisphosphate Receptor type 1 (ITPR1) gene and upregulates its expression, triggering an ER-mitochondria calcium (Ca2+) transfer, which is involved in MYC-induced cell death and senescence. Supporting a tumor suppressive role of MYC / ITPR1 axis, ITPR1 expression is generally decreased in cancer and reactivation of this pathway induces cancer cell death. Nevertheless, some cancer cells, generally expressing high levels of MYCN and / or MYC, also express high level of ITPR1, which correlates with high expression of BCL2, encoding an inhibitor of ITPR1. Strikingly, in high-risk MYCN-amplified neuroblastoma, ITPR1 expression is controlled by NMYC and its level correlates with worse patient survival. In these cells, blocking the interaction between BCL2 and ITPR1, via an BCL2-BH4 domain inhibitor induces mitochondrial Ca2+ accumulation and cell death, and decreases tumor size. Thus, the present invention relates to a method for treating MYChigh cancer, and in particular NMYC--amplified neuroblastoma in a subject by administering an BCL2-BH4 domain inhibitor.

The invention relates to application of STX10 in treatment and prognosis evaluation of kidney cancer. The key effect of the STX10 in the renal clear cell carcinoma is emphasized, and the fact that the expression of the STX10 in ccRCC is remarkably up-regulated is revealed. Overexpression of the STX10 promotes proliferation and migration of ccRCC cells, is an independent risk factor of poor OS, PFI and DSS of the ccRCC and is closely related to poor immunotherapy response, it is clear that the STX10 can serve as a prognosis biomarker and a treatment target of the ccRCC, and development of kidney cancer can be obviously inhibited, the survival rate of patients can be prolonged and prognosis can be improved by inhibiting the STX10 or reducing the biological activity of the STX10. Related molecular mechanisms in the occurrence and development process of the kidney cancer are enriched, sufficient scientific basis and theoretical basis are provided for exploring new kidney cancer diagnosis, prognosis judgment and treatment molecular targets and developing new targeted drugs, accurate treatment can be better achieved, and important social value and scientific significance are achieved.

The invention discloses a prognosis marker and a dynamic prognosis prediction method for colorectal cancer liver metastasis. The method comprises the following steps: S1, acquiring clinical pathological characteristic data of a patient with colorectal cancer liver metastasis and a longitudinal laboratory marker measured during post-operation follow-up visit; s2, extracting a change trend of the longitudinal laboratory marker by utilizing multivariable function principal component analysis to obtain a principal component score; s3, training a random survival forest model by using the principal component score and the clinical pathological feature data to obtain a dynamic prediction model; s4, dynamically updating the score of the principal component based on a newly collected laboratory marker of postoperative follow-up visit of the patient, and outputting dynamic risk assessment results of the progression-free lifetime and the total lifetime of the patient through a dynamic prediction model; according to the method, the prediction model capable of dynamically evaluating the survival risk of the patient is constructed and updated by fusing the dynamic longitudinal laboratory marker and the static clinical pathological characteristics.

Methods are described for treating a cancer patient with a neutrophil-to-lymphocyte ratio (NLR) greater than 3, comprising administering a nonsteroidal glucocorticoid receptor antagonist (GRA) to the cancer patient, effective in reducing the patient's NLR. The methods include administering a nonsteroidal GRA and cancer therapy to the cancer patient, effective in reducing the patient's NLR and enhancing the cancer patient's treatment. The GRA may be administered orally. The nonsteroidal GRA may be a nonsteroidal compound comprising a fused azadecalin heteroarylketone structure (e.g., relacorylant) or a fused octahydro azadecalin structure (e.g., exicorylant).Cancer treatment may include chemotherapy, immunotherapy, radiation therapy, administration of an anti-angiogenic agent, administration of a growth factor inhibitor, and surgery. These methods can enhance cancer treatment, improve the prognosis of cancer patients, improve cancer patient survival, and provide beneficial clinical effects and advantages for the patient.

The invention discloses a clinical prediction model construction method and system for treating oligometastatic non-small cell lung cancer through radioactive particle implantation, and relates to the technical field of medical model construction, and the method comprises the steps: obtaining patient data, taking survival time as a main endpoint event, employing a Cox regression model to carry out single-factor analysis on factors affecting patient prognosis, and obtaining a single-factor analysis result; determining the relationship between each clinical feature and survival time, and incorporating the important factors influencing the prognosis into multi-factor Cox regression analysis to determine independent risk factors influencing the survival prognosis of the patient; generating a first clinical prediction model according to each independent risk factor nano-construction; cD3 + AC, CD4 + AC, CD8 + AC, combined treatment, complete treatment and the number of metastatic lesions are taken as variables together to be included in construction of a clinical prediction model, and a second clinical prediction model is manufactured and generated. Independent risk factors influencing the prognosis of a radioactive particle implantation treatment few-metastatic NSCLC patient are confirmed, the clinical prediction model is established, the treatment prognosis can be accurately predicted, and the treatment cost is reduced. And screening of dominant benefit people is facilitated.

This invention provides an irinotecan-conjugated polypeptide compound, its preparation method, and its application, belonging to the field of biomedical technology. Irinotecan (Dxd) is the active pharmaceutical ingredient. Irinotecan belongs to the topoisomerase I inhibitor class—camptothecin derivatives—and is a semi-synthetic small molecule chemotherapeutic drug capable of directly killing cancer cells in the active division phase and inhibiting tumor growth. TYLCTACDYTHH is a highly effective PDPN-targeting peptide that can effectively target the PDPN site in human glioblastoma. In vitro and in vivo experimental results show that the conjugated compound of this invention can significantly inhibit the proliferation of primary glioblastoma cells and effectively inhibit the growth of intracranial orthotopic xenografts in NOD-SCID mice, demonstrating good anti-tumor activity. This compound shows promise for targeted therapy of glioblastoma and has the potential to prolong patient survival.

The invention provides application of EMD638683 and a compound thereof as a therapeutic target to preparation of a medicine for treating gastric cancer, relates to the technical field of biomedicine, and is technically characterized by providing application of EMD638683 (R-Form) as a therapeutic target to preparation of a medicine for treating gastric cancer. According to the application, a specific verification test proves that EMD638683 (R-Form) is targeted to an N40 glycosylation site of LT beta R, a TRIM28 / PCBP2 / SARM1 pathway is specifically blocked, and the off-target toxicity is low. After the cRGD modified liposome is delivered, the tumor specific enrichment can be realized, the bioavailability is improved, the damage to normal organs is reduced, and the safety is good. The medicine can effectively reverse gastric cancer radiotherapy resistance, is adaptive to radiotherapy non-responders with high LT betaR expression, assists in precise stratified treatment, and improves the survival rate of patients. A traditional DNA repair related mechanism is broken through, the sensitization effect is achieved by regulating and controlling a pyroptosis pathway, and a brand new direction is provided for research and development of gastric cancer radiotherapy sensitization drugs.

The invention discloses a bladder cancer combined treatment method based on CTSE enhancement. According to the application, research finds that CTSE is a key factor related to ICB reaction and patient survival rate improvement. Functional studies find that CTSE improves the curative effect of ICB by enhancing MHC expression, so that antigen presentation, T cell activation and anti-tumor immunity are promoted, and the anti-inflammatory phenotype of macrophages is adjusted at the same time. Importantly, overexpression of the CTSE can further enhance the immunoreaction for blocking the PD-1, has the effect of synergistically treating the bladder cancer, and provides a new idea for effective treatment of the bladder cancer.

The invention belongs to the technical field of intelligent drug delivery control, and particularly discloses a colon tumor drug delivery method and system based on AI control, and the method comprises the steps: collecting and preprocessing whole genome sequencing, radiomics, clinical pathology and historical drug treatment response data of a patient, extracting tumor image features, and carrying out drug delivery. The method comprises the following steps: screening oxaliplatin response related gene mutation markers by combining Lasso regression with a Cox model, forming feature vectors by t-SNE dimensionality reduction clinical pathological data, training a model through a gradient boosting tree and deep neural network fusion algorithm, taking oxaliplatin response probability as output, and recommending an FOLFOX scheme or an FULV scheme according to a model result. According to the method, precision and individuation of chemotherapy are achieved, unnecessary toxic and side effects are reduced, the life quality and lifetime of a patient are improved, and the problems that in the prior art, dependence on experience, single data dimension, no dynamic adjustment mechanism and poor stability are solved. Therefore, the problems of low response rate of oxaliplatin and obvious side effect are solved.

The invention belongs to the field of tumor molecular biology, and particularly relates to application of FBXO44 protein as a target in screening of products for diagnosis, treatment and prognosis of gastric cancer, and the amino acid sequence of the FBXO44 protein is shown as SEQ ID NO.2. The expression level of the FBXO44 protein is detected according to pathology and is used as a basis for judging disease progression, prognosis and survival of gastric cancer patients. The invention further provides application of down-regulation of the FBXO44 protein level in preparation of drugs for treating gastric cancer, down-regulation of the FBXO44 can effectively inhibit growth and metastasis of gastric tumors, a new scheme is provided for disease diagnosis and drug development of gastric cancer, and the current situations that the number of targeted therapeutic targets for gastric cancer is small, and available effective drugs are small are solved to a certain extent clinically. The invention provides a new scheme and strategy for the treatment of gastric cancer, and provides possibilities for preparing new targeted drugs and improving the survival and prognosis of patients.

The present application relates to the field of biotechnology, in particular to the clinical application of SHOX2, PTGER4 methylation and serum marker cyfra21-1 combined detection in the diagnosis of malignant pleural mesothelioma. The present application first found that the combined detection of SHOX2, PTGER4 DNA methylation and serum marker cyfra21-1 can significantly improve the diagnosis rate of malignant pleural mesothelioma, reduce misdiagnosis, improve diagnostic accuracy, and improve patient survival rate.

This invention belongs to the field of biomedical technology and provides the application of 16'-DEC in the preparation of anti-hepatocellular carcinoma drugs. This bisindole alkaloid compound can inhibit the proliferation and migration of hepatocellular carcinoma cells and can produce a synergistic anti-tumor effect with lenvatinib, solving the problems of limited efficacy and insufficient patient survival benefit of existing targeted drugs in hepatocellular carcinoma treatment. Experiments have confirmed that 16'-DEC exerts its anti-tumor effect by directly inhibiting mTOR kinase activity. This compound can specifically bind to mTOR protein, significantly upregulate the expression of autophagy-related genes by blocking the mTOR signaling pathway, and induce autophagic cell death in tumor cells. In vitro and in vivo experiments have demonstrated that inhibiting autophagy can reverse the anti-cancer effect of 16'-DEC, indicating that its pharmacodynamic mechanism mainly depends on the activation of the autophagy pathway. This invention is applicable to the development of novel targeted therapies for hepatocellular carcinoma, especially specific inhibitors of the mTOR signaling pathway.

w The invention discloses a detection method for predicting efficacy and prognosis of immunotherapy for small cell lung cancer based on ATAD2 expression, and relates to the technical fields of molecular biology and cancer treatment. The method comprises the following steps: extracting a tumor tissue sample of a small cell lung cancer patient through surgical resection; Detecting the tumor tissue sample to obtain ATAD2 expression level, wherein the detection adopts an immunohistochemical staining method. According to the invention, the expression level of ATAD2 in tumor tissues of small cell lung cancer is quantitatively evaluated by immunohistochemical detection technology, and the molecular marker information is systematically correlated with the clinical and pathological characteristics of patients by combining Cox regression analysis and Logistic regression analysis, so that the comprehensive prediction of immunotherapy efficacy and patient survival time is realized.

The present application relates to the field of biotechnology, and in particular, discloses a marker for diagnosing sepsis and use thereof. The present application first discovers that serum METRNL can predict the survival of sepsis patients, and METRNL can be used as a marker for predicting the survival of sepsis patients. The AUC of METRNL combined with PCT for diagnosing sepsis can reach 1. Compared with the diagnosis effect of a single marker, the AUC value and specificity of METRNL combined with PCT are significantly improved, and sepsis patients can be completely distinguished, which is an ideal sepsis diagnostic index.

This invention discloses a bone repair scaffold system for tumor early warning and non-invasive treatment, comprising a bone repair scaffold, a microenvironment monitoring RFID module, a controlled drug release module, a signal reading system, and a cloud processor. The bone repair scaffold is shaped to fit the wound site and has a porous internal structure. The microenvironment monitoring RFID module is disposed on the internal and external surfaces of the bone repair scaffold. The controlled drug release module includes a microwave induction module and a drug storage unit. An external microwave excitation causes the microwave induction module to heat up, stimulating the thermoresponsive drug storage unit to release the drug. The signal reading system communicates with the microenvironment monitoring RFID module via radio frequency and is interconnected with the cloud processor via the internet. This invention can promote bone repair while rapidly monitoring tumor recurrence, alerting patients to seek medical attention early. Simultaneously, the drug release module enables non-invasive treatment, reducing patient suffering, improving quality of life, and prolonging survival.

The application discloses a method for screening B-cell lymphoma biomarkers and belongs to the technical field of bioinformatics. The application aims to provide a more accurate and simple method for screening B-cell lymphoma biomarkers. The application provides a method for screening B-cell lymphoma biomarkers, which utilizes scRNA-seq data of Burkitt lymphoma, diffuse large B-cell lymphoma and follicular lymphoma, splenic marginal zone lymphoma and bulk RNA-seq data of Burkitt lymphoma and diffuse large B-cell lymphoma database to screen the relationship between T / NK and myeloid cells all cell subpopulation infiltration ratio and patient survival period. The application provides a theoretical basis for screening lymphoma disease survival analysis.

This invention provides a liver-targeting glycoligand molecule modified with dual-antenna GalNAc and its drug delivery system. This liver-targeting glycoligand molecule and its drug delivery system, through ASGPR recognition, can maximize the concentration of therapeutic drugs in liver tumor parenchymal cells, thereby improving the targeting of drug distribution, increasing the therapeutic index, reducing systemic toxicity, and improving patient survival time and quality of life. The liver-targeting glycoligand molecule of this invention is synthesized using an enzymatic synthesis method, which involves fewer synthesis steps, mild enzymatic reaction conditions, high regioselectivity, high reaction efficiency, is environmentally friendly, and has low production costs, making it highly promising for industrialization.

The application belongs to the technical field of medical treatment and discloses a cancer prognosis marker combination and application thereof, which comprises the following markers: CD68, PD-L1, PAN-CK, HLA-DR, CD45, CD11c and Collagen I. The cancer prognosis evaluation model trained by using the 7-marker combination exhibits superior prognosis accuracy compared to a model relying on high-dimensional multi-omics data. The application proposes a multiple immunohistochemistry (mIHC) cancer prognosis evaluation detection product based on the above-mentioned cancer prognosis marker combination. Satisfactory prognosis results are achieved by using only two five-marker combinations. The method exhibits high accuracy in predicting the survival of gastric cancer patients, so that it becomes a low-cost, high-benefit and scalable solution for other cancers.

This invention discloses a urinary metabolic biomarker composition for early lung cancer diagnosis and its application. The urinary metabolic biomarker composition for early lung cancer diagnosis includes ethanolamine, catechol, isomaltose, and nicotinamide. The urinary metabolic biomarker composition provided by this invention offers rapid detection, high specificity, high sensitivity, and high predictive accuracy for early lung cancer diagnosis. It is non-invasive and non-surgical, facilitating earlier intervention and targeted, individualized precision treatment for patients, thereby improving patient survival rates. Furthermore, the urinary metabolic biomarker composition uses urine samples for early lung cancer diagnosis, which has advantages such as easy collection, low processing costs, and long-term cryopreservation.