130 results about "Patient survival" patented technology

The invention discloses a prognosis evaluation method and system for II-III stage colorectal cancer patients, and relates to the technical field of wisdom medicines.The prognosis evaluation method comprises the steps that variables related to survival prognosis of the patients are screened, potential variables influencing RFS and OS of the patients are obtained, and prognosis outcomes of the colorectal cancer patients are evaluated by evaluating relevance between various indexes and prognosis outcomes of the colorectal cancer patients and correcting confounding factors; screening out indexes having important value for the model; the screened indexes and machine learning algorithms are utilized to construct RFS and OS prognosis models of the CRC patients, the RFS and OS prognosis models are used for performing prognosis evaluation on the CRC patients in the II-III stages, and the machine learning algorithms comprise six types of LR, RF, XGB, SVC, MLP and GNB. By integrating clinical data and a machine learning technology, an efficient and accurate tool is provided for prognosis evaluation of the II-III stage colorectal cancer patient, and a scientific basis is provided for making clinical decision support and an individualized treatment scheme.

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.

The present invention proposes an intelligent management system for chronic comorbidities in the elderly, including a comprehensive elderly assessment module, a multidisciplinary collaborative diagnosis and treatment module, a multiple medication risk prediction management module, a chronic disease intelligent follow-up management module, and a health science knowledge base module. The comprehensive elderly assessment module inputs the patient's individual chronic disease profile into a risk factor prediction model for prediction and outputs a comprehensive assessment result. The multidisciplinary collaborative diagnosis and treatment module is used by the first-visit / admitting physician to coordinate multidisciplinary physicians for collaborative diagnosis and treatment. The multiple medication risk prediction management module is used to provide patients with comprehensive diagnosis, multiple medication management, and risk prediction through the collaborative diagnosis and treatment. The chronic disease intelligent follow-up management module performs follow-up tracking, health risk prediction, and review prompts for patients. The health science knowledge base module matches and pushes corresponding health education knowledge. The present invention improves the accuracy and timeliness of risk assessment, prediction, and management of chronic comorbidities in the elderly, and plays a good auxiliary role in improving the quality of life of patients.

The invention discloses an individualized drug dose optimization method based on causal traceability, which improves the accuracy and clinical adaptability of dose regulation and control through a multi-modal space-time alignment and feature decoupling technology, causal reasoning modeling and causal effect quantification and individualized dose optimization, can effectively reduce adverse reactions in the treatment process, and improves the treatment efficiency. The life cycle of a patient is prolonged, the life quality is improved, meanwhile, the long-term medication cost is reduced, and wide clinical application prospects and popularization values are achieved.

The invention relates to a composition and related methods for reducing tumor volume and / or increasing the survival of a cancer patient. The composition comprises a recombinant MVA encoding a Tumor Associated Antigen (“TAA”) as well as 4-1BBL and / or CD40L and can be administered to a subject in any suitable manner, including by intravenous and / or intratumoral administration.

The invention relates to a method and a model for predicting the lifetime of a non-tumor peaceful patient based on clinical data. The method comprises the following steps of: collecting and sorting previous hospitalization clinical data of a non-tumor peaceful treatment death patient, and carrying out standardized pretreatment; carrying out attribute selection and reduction on the high-dimensional data from two perspectives of correlation and redundancy by using a maximum information coefficient (MIC); the method comprises the following steps: respectively carrying out attribute weighting by applying a Pearson correlation coefficient and a Spearman rank correlation coefficient; a weighted naive Bayes classification model is used for modeling; according to the method, clinical factors related to the lifetime of the non-tumor Anning treatment patient can be objectively analyzed and predicted, and guidance is provided for Anning clinical admission standard formulation and early and later treatment plans. The model can be realized in the form of a doctor workstation and the like, and the medical work efficiency and accuracy can be greatly improved.

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 present disclosure provides an establishment and use of index for evaluating malignancy level of tissue microenvironment, which can be used to detect the potential of an individual to develop precancerous lesions and to predict the recurrence of tumors without relying on patient survival data.

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.

The present invention discloses a method for prognostic evaluation of the survival period of lung adenocarcinoma patients, a computer device, and a storage medium. The prognostic index is calculated by the following formula: Prognostic index = β1 * TNM stage + β2 * ERSS; where β1 and β2 are weight coefficients, and ERSS is the endoplasmic reticulum stress score. The present invention evaluates the correlation between endoplasmic reticulum stress-related genes and the prognosis of lung adenocarcinoma patients according to a machine learning method, establishes an endoplasmic reticulum stress score, and combines the endoplasmic reticulum stress score with the TNM stage to establish a survival prediction model. Experiments show that the survival prediction model of the present invention has a good predictive effect on the overall survival period, and solves the problem that the TNM stage alone cannot accurately evaluate the survival period of patients.

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.

The present invention provides the use of pyruvate in the treatment of lipodystrophy syndrome, belonging to the technical field of endocrine and metabolic disease treatment. This invention proposes for the first time the use of pyruvate, such as sodium pyruvate and potassium pyruvate, in the treatment of lipodystrophy syndrome. It has been found that pyruvate can restore atrophic adipose tissue and improve the progression of diabetic complications and acute pancreatitis associated with hypertriglyceridemia, thereby increasing patient survival and improving overall prognosis.

The invention discloses a gene marker panel for diagnosing PCNSL based on cerebrospinal fluid ctDNA, and relates to the technical field of gene detection, the technical key point is that the gene marker panel comprises MYD88, PIM1, CD79B, GNA13, IRF4, DTX1, KMT2D and B2M genes, and PCNSL diagnosis and prognosis evaluation are realized through targeted capture sequencing and a machine learning model. The specificity of the kit in MYD88 wild type PCNSL cases reaches 100%, the BTK inhibitor treatment response and the total lifetime of patients can be predicted, tiny residual focuses can be detected, and recurrence can be warned in advance. The invention further provides a detection kit, a prognosis evaluation system and online webpage application, the quantity demanded of samples is small, the detection sensitivity is high, the specificity is high, and a powerful tool is provided for PCNSL diagnosis and treatment.

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 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.

Understanding the function of rare non-coding variants represents a significant challenge. Using MapUTR, a screening method, we studied the function of rare 3' UTR variants affecting mRNA abundance post-transcriptionally. Functional MapUTR variants were enriched in microRNA- or protein-binding sites and may underlie outlier gene expression in tumors. Further, we introduce untranslated tumor mutational burden (uTMB), a metric reflecting the amount of somatic functional MapUTR variants of a tumor and show its potential in predicting patient survival. Through prime editing, we characterized three variants in cancer-relevant genes (MFN2, FOSL2, and IRAK1), demonstrating their cancer-driving potential. Our disclosure elucidates the function of tens of thousands of non-coding variants, nominates non-coding cancer driver mutations, and demonstrates methods for their use in the management of cancers.

The application discloses an automatic prognosis system for nasopharyngeal carcinoma primary focus and lymph node nuclear magnetic resonance image, which comprises a data preparation module, a segmentation module, a feature sharing module, a data processing module and a prognosis module; the data preparation module puts all nuclear magnetic resonance images of a patient into a folder in a patient unit; the segmentation module is used for processing data obtained by the data preparation module; the feature sharing module uses an expansion convolution group to reduce dimensions of each layer multi-scale deep features obtained by the segmentation module but maintains the scale unchanged; the data processing module uses a prediction map generated by the segmentation module and the nuclear magnetic resonance image obtained by the data preparation module to perform a mask strategy; the prognosis module is used for processing a lesion map obtained by the data processing module and the multi-scale deep features processed by the feature sharing module to obtain a prediction value, finally uses the prediction value to obtain a survival probability of the patient, and realizes risk stratification of the patient and gives a suggestion on whether to accept chemotherapy; the application can automatically realize prognosis prediction of the nasopharyngeal carcinoma patient, realizes automation in the whole process and avoids manual participation.

This application discloses a pressure sensing system and an external monitoring device. The external monitoring device includes: a resonant circuit comprising a variable capacitor and a resonant coil connected in series; a power amplifier with its first terminal connected to the variable capacitor and its second terminal connected to the resonant coil; and a processor with its first terminal connected to the third terminal of the power amplifier and its second terminal connected to the variable capacitor. The processor generates an AC signal, and the power amplifier amplifies the AC signal and uses the amplified AC signal to drive the resonant circuit. The processor also controls the capacitance value of the variable capacitor to apply a broadband magnetic field to an implanted device, thereby detecting the resonant frequency with the implanted device and obtaining the atrial filling pressure detected by the implanted device through the resonant frequency. Through this method, the external monitoring device provided by this application can monitor atrial filling pressure in real time, promptly detect abnormal atrial filling pressure values, and improve patient survival rates.