90 results about "Observed Survival" patented technology

The invention relates to the field of medical information management, in particular to a children malignant tumor treatment process and follow-up visit electronic management system and a children malignant tumor treatment process and follow-up visit electronic management method. The method comprises the steps that the body surface area of a patient is calculated, and the chemotherapy drug dosage in each cycle is calculated based on the body surface area of the patient; performing cycle-by-cycle accumulation on the chemotherapeutic drug dose in each cycle to obtain an actual accumulated dose; calculating and standardizing the specific numerical value of each curative effect index based on the actual accumulated dose, and calculating a comprehensive therapeutic effect score by combining the importance weight of each curative effect index; and calculating the disease-free survival rate based on the actual accumulated dose and the comprehensive treatment effect score. The problems that at present, most medicine dosage calculation methods neglect the special physiological features of children, a treatment monitoring system lacks dynamic adjustment support in the treatment process, a follow-up management system is insufficient in quantitative analysis capacity for long-term prognosis, and personalized long-term health management suggestions cannot be provided for patients are solved.

The invention relates to the technical field of medical care, in particular to a tumor prognosis prediction method based on tumor database text recognition, which comprises the following steps: S1, data collection and preprocessing: collecting medical data related to tumors from a plurality of tumor databases; s2, text information extraction: performing information extraction on clinical text data; s3, predicting a tumor metastasis path: predicting the metastasis path by utilizing the graph neural network model, predicting a potential metastasis path of the tumor, and analyzing the influence of different metastasis paths on the prognosis of the patient; s4, prognosis evaluation and risk prediction: predicting the tumor prognosis risk in combination with the medical data of the patient, and comprehensively evaluating the five-year survival rate and the recurrence risk of the patient. According to the method, the medical institution can be assisted in optimizing resource allocation, a scientific personalized diagnosis and treatment scheme is formulated, and the life quality and the treatment effect of the patient are remarkably improved.

The invention discloses a liver cancer post-transplantation muscle loss prediction method and device, and belongs to the technical field of biomedicine, and the method comprises the steps: constructing an unbalanced random forest model comprising a plurality of decision trees, independently predicting through the plurality of decision trees to obtain a plurality of high-risk or low-risk dichotomy prediction results, taking the proportion of the decision tree with the prediction result being high risk in all decision trees as the prediction probability of muscle loss after liver cancer liver transplantation finally output by the model; performing risk classification according to the prediction probability, and judging the postoperative muscle loss level of the patient as high risk, medium risk or low risk; constructing a decision tree model to layer prognosis of patients, and dividing the patients into a high-risk group, a medium-risk group and a low-risk group; and carrying out survival rate prediction in combination with liver transplantation standards and prognosis layering results of patients. According to the method, the muscle loss level after the liver transplantation can be accurately predicted, and a clinician can make a personalized treatment scheme and perform scientific management after the transplantation.

The invention discloses a drug screening method based on endotoxin attack, and relates to the technical field of drug screening, in the process of drug screening by using an experimental animal, firstly, an experimental animal model with diseases is established, a certain dose of endotoxin is given to the experimental animal for a period of time after the model is successfully built, and then the experimental animal is subjected to drug screening. And establishing evaluation indexes by utilizing a mathematical formula, and comprehensively evaluating the effectiveness and the safety of the medicine. The evaluation indexes comprise an endotoxin attack survival rate, a survival disease control rate and a survival disease control index. The method can be applied to screening of drugs for treating different diseases, and a more scientific and reliable screening method is provided for research of preclinical drugs or treatment methods.

The invention discloses a lung cancer patient dynamic survival prediction model based on multi-dimensional clinical data, and relates to the technical field of cancer survival prediction, and the key point of the technical scheme is that the dynamic survival prediction model comprises a core model module and a data support module, the core model module comprises a graph convolutional neural network model, a long-short-term memory network model and a generative large language model, and the data support module comprises three data processing systems, namely a time-dependent variable, a time independent variable and a data balancing strategy. According to the dynamic survival prediction model based on combination of traditional Chinese medicine and western medicine, abundant multivariate data in EHR can be fully utilized, individualized and precise diagnosis and treatment of the patient can be realized, artificial intelligence is assisted, and dynamic prediction of the survival rate of the lung cancer patient in one year, two years and three years can be effectively realized.

The invention provides a metabolic marker combination for ovarian cancer diagnosis and application thereof, and belongs to the technical field of in-vitro diagnosis. The invention provides a metabolic marker combination for ovarian cancer diagnosis. The metabolic marker combination comprises phosphatidylcholine 34: 4, threonic acid, phosphatidylcholine 32: 2, pyroglutamic acid, phosphatidylcholine 36: 6 and ceramide d38: 2. According to the metabolic marker combination, plasma samples of a healthy subject and an ovarian cancer patient are analyzed, a group of biomarkers with significant difference are screened from the plasma samples, and the metabolic marker combination is a non-invasive, non-invasive and high-accuracy metabolic marker combination for distinguishing the healthy subject and the ovarian cancer patient. The method can be used for constructing a diagnostic model for distinguishing healthy subjects from ovarian cancer patients. The metabolic marker combination disclosed by the invention can be used for improving the diagnosis and evaluation capability of ovarian cancer patients, so that early discovery, early diagnosis and early intervention can be realized clinically, and the survival rate of the ovarian cancer patients is improved.

The invention relates to the technical field of biological medicine, in particular to construction and application of a candida patient death risk prediction model. The model is based on traditional SOFA scoring, core variables are screened through single-factor and multi-factor logistic analysis and LASSO regression analysis, respiratory system, blood coagulation function and circulatory system SOFA scoring and key indexes such as the maximum lactic acid value, the minimum albumin value and the maximum blood urea nitrogen (BUN) are integrated, and a prediction model is constructed through logistic regression. Through multi-center data verification, the AUC of an internal verification queue reaches 0.826, the AUC of an external verification queue is 0.813, and a calibration curve shows that the predicted death rate is highly consistent with the actual death rate. Patients can be divided into a high risk group and a low risk group through a 13-score threshold value, and the 28-day survival rate difference is significant (plt; 0.05) of the method. An efficient and convenient early risk assessment tool is provided for critical candida patients, and clinical precise intervention and prognosis improvement are assisted.

The invention discloses a postoperative pathology assessment method, survival prediction method and system for esophageal cell carcinoma, and the method comprises the steps: obtaining and preprocessing WSI image data, obtaining cut small images of the WSI image data in a low-magnification view angle, a medium-magnification view angle and a high-magnification view angle, and sequentially inputting the cut small images into a selective spatial state model through feature extraction; updating the diagnosis states through a long and short term memory network, and adjusting the contribution degree of each diagnosis state through a selective memory weight; fusing all diagnosis states, and outputting the probability of tissue types through a full connection layer and a Softmax function; calculating the percentage of the residual living tumor, and obtaining a pathological assessment result according to the percentage of the residual living tumor. According to the method, the diagnosis process of doctors can be simulated, the workload of pathologists can be reduced, the evaluation consistency and universality are improved, and the survival rate can be predicted and clinical treatment decision making can be assisted through the constructed survival prediction model.

The invention belongs to the technical field of biological information, and provides an ampulla cancer patient prognosis model based on machine learning and a construction method thereof. According to the method, clinical information of an ampulla cancer patient is obtained through a cancer statistical database, clinical characteristics independently related to survival of the patient are analyzed and positioned through single-factor and multi-factor COX, a prognosis model of the ampulla cancer is established through multiple machine learning algorithms according to the clinical characteristics, and the method can be used for survival prediction of the patient. The method has the advantages of being rapid in prediction, high in accuracy and easy and convenient to use, gene determination does not need to be specially conducted on tissue or blood sampling of the patient, accurate survival rate judgment can be achieved only through clinical information, a clinician can make a personalized treatment scheme of the patient according to the prognosis state, and therefore the survival rate of the patient is increased.

The invention discloses a generic cancer prognosis biomarker and an application thereof in evaluating the poor prognosis of the generic cancer, which can predict the outcome of a generic cancer type cancer, can be used as a reliable biomarker of a treatment target, and helps clinicians to predict the disease progression and survival rate of patients. The VANGL1 gene is a VANGL1 molecule and comprises a VANGL1 gene and a protein coded by the VANGL1 gene.

The invention relates to a prediction model for judging prognosis and immunotherapy response of ovarian cancer based on machine learning. The prediction model comprises the following steps: determining gene expression quantities of eight markers, namely ANGPTL4, AREG, CXCL10, FANCB, GMPR, IGF1, LYG1 and WNK 1. The invention also provides a gene combination kit for predicting the prognosis and immunotherapy response of the ovarian cancer and application of the gene combination kit. The invention provides a prediction model aiming at ovarian cancer, which is used for judging the prognosis of a patient, can assist in guiding immunotherapy and prognosis judgment, and helps to realize personalized treatment aiming at a specific patient. Experiments and tumor big data support the prognosis model, the prognosis and immunotherapy response of a patient can be effectively predicted by integrating various biological and clinical data, powerful support can be provided for individualized treatment, a treatment scheme can be optimized, and the survival rate and life quality of the patient can be improved.

The invention relates to the technical field of biological medicine, in particular to application of UL16 binding protein 2 (ULBP2) as a colorectal cancer (CRC) marker. Diagnostic performance of ULBP2 expression is analyzed and evaluated through a subject working characteristic (ROC) curve, the result shows that the ULBP2 is remarkably and highly expressed in CRC tissue, the high expression level is related to remarkably shortening of the overall survival (OS) of a patient, and good diagnosis and prognosis values of the ULBP2 are shown. Functional experiment results show that knock-down of the ULBP2 gene significantly reduces the survival rate and proliferation ability of HCT-8 and LoVo cells, and effectively inhibits tumor cell migration, indicating that the ULBP2 inhibitor has good application prospects in development of colorectal cancer treatment drugs. The invention provides a new biomarker and a treatment target for diagnosis, prognosis and treatment of colorectal cancer.

The invention relates to the field of biological medicines, and discloses application of serum EPSTI1 in preparation of a product for diagnosing liver cancer and evaluating whether a liver cancer patient is resistant to a revatinib or not. The invention provides a detection means which is convenient and rapid in sampling, small in risk and relatively low in cost, the content of EPSTI1 in serum is detected through an enzyme-linked immunosorbent assay, a liver cancer patient and a non-liver cancer patient can be effectively distinguished, and a lovatinib drug-resistant liver cancer patient and a non-drug-resistant liver cancer patient can be effectively distinguished. Prognosis analysis shows that the serum EPSTI1 can be used as a marker for evaluating the prognosis state of the liver cancer patient taking the lenvatinib, and has important significance in improving the survival rate of the liver cancer patient.

The application discloses application of a reagent for detecting AHSP protein in preparation of a product for diagnosing prostate cancer, and relates to the technical field of biological medicine; through proteomic analysis, ELISA test on urine protein of prostate cancer patients, clear cell renal cell carcinoma patients and normal healthy men, and immunohistochemical analysis on file wax blocks of the prostate cancer patients, normal prostate tissues around the cancer, prostate tissues of prostatic hyperplasia and clear cell renal cell carcinoma tissues, it is found that the AHSP protein has a certain relationship with the occurrence and development of prostate cancer, and can be used for assisting diagnosis of the prostate cancer; and in urine detection, AHSP has higher sensitivity than PSA, and when PSA is negative or serum PSA is low, AHSP can form complementation with ASHP, thereby helping to improve the diagnosis rate and non-invasive screening ability; through research on the relationship between the AHSP protein and the prostate cancer, the diagnosis rate of patients can be improved, non-invasive reexamination after treatment is facilitated, drug use is guided, and the survival rate is improved.

The present invention relates to the field of molecular diagnosis, and in particular to colorectal cancer markers and detection kits. The present invention provides markers, primer probe sets, and products and detection methods for colorectal cancer, realizes multi-marker joint detection, improves detection performance, and helps to use blood for early screening and early diagnosis of colorectal cancer. The detection method is affordable and has good compliance, and is expected to increase the screening detection rate of colorectal cancer, improve the survival rate of colorectal cancer, and improve prognosis.

The present invention relates to the technical field of medical care, and particularly relates to a tumor prognosis prediction method based on tumor database text recognition, comprising the following steps: S1, data collection and preprocessing: collecting tumor-related medical data from multiple tumor databases; S2, text information extraction: extracting information from clinical text data; S3, tumor metastasis path prediction: using a graph neural network model to predict the metastasis path, predicting the potential metastasis path of the tumor, and analyzing the impact of different metastasis paths on the prognosis of patients; S4, prognosis evaluation and risk prediction: combining the medical data of patients to predict the tumor prognosis risk, and comprehensively evaluating the predicted value of the five-year survival rate and the predicted value of the recurrence risk of patients. The present invention can assist medical institutions in optimizing resource allocation, formulating scientific personalized diagnosis and treatment plans, and significantly improving the survival quality and treatment effect of patients.

The invention discloses a pancreatic cancer diagnosis marker and application thereof, and belongs to the technical field of biological medicines, a serological marker for pancreatic cancer diagnosis is thymosin beta-10, namely Tbeta10, a biomarker for pancreatic cancer diagnosis is a gene TMSB10 for coding the Tbeta10, the gene is 9168 in an NCBI database, a qRT-PCR upstream primer of the TMSB10 is shown as SEQ ID NO.1, a qRT-PCR downstream primer of the TMSB10 is shown as SEQ ID NO.2, the qRT-PCR downstream primer of the TMSB10 is shown as SEQ ID NO.1, the qRT-PCR upstream primer of the TMSB10 is shown as SEQ ID NO.1, and the qRT-PCR downstream primer of the TMSB10 is shown as SEQ ID NO.2. Tbeta10 protein or TMSB10 gene high expression serves as a biomarker for diagnosis and poor prognosis of a pancreatic cancer patient, it is determined that the content of Tbeta10 in serum of the pancreatic cancer patient is remarkably increased, the Tbeta10 protein or TMSB10 gene high expression serves as a serum marker for diagnosis and curative effect and prognosis monitoring of the pancreatic cancer, and the total survival rate of the pancreatic cancer patient is reduced; the TMSB10 is used as a biomarker for poor prognosis of pancreatic cancer patients.

The present disclosure relates to predicting a survival rate of a patient with cancer following a treatment. In some embodiments, one or more processors: apply a large language model to a clinical report to extract a plurality of clinical features; acquire a pre-treatment image and a post-treatment image of the patient with cancer; apply a segmentation algorithm on an annotated volume of interest (VOI) of the pre-treatment image to obtain a first segmented VOI; apply the segmentation algorithm on the annotated VOI of the post-treatment image to obtain a second segmented VOI; determine a plurality of radiomics features and a plurality of deep learning features from the first segmented VOI and the second segmented VOI; and apply a machine learning model to the plurality of clinical features, the plurality of radiomics features, and the plurality of deep learning features to predict the survival rate of the patient with cancer.

The application discloses a methylation marker combination for detecting early gastric cancer and application thereof. The marker combination comprises 146 methylation sites of 11 DNA regions and a transformation rate control marker region, and the methylation levels of the target regions are significantly different between early gastric cancer patients and healthy people. The kit of the application contains reagents for detecting the methylation degree of the above sites, and supports various high-throughput sequencing methods. The detection system adopts a logistic regression model to calculate a prediction value, multiplies the average methylation rate of each region by a corresponding weight coefficient, adds them together, and compares the sum with a threshold value to make a positive judgment. The application realizes high sensitivity and high specificity for non-invasive early detection, is particularly suitable for non-invasive screening of plasma free DNA samples, can effectively improve the survival rate of gastric cancer patients, can significantly reduce medical expenses, and has a wide clinical application and industrial utilization prospect.

The present invention relates to the discovery of an agent for measuring the expression level of a protein that induces the acquisition of metastatic ability by tumor cells, or a gene encoding the same, and a method for preventing or treating metastatic cancer by controlling the expression of the protein or gene. The present invention provides effective biomarkers for metastatic cancer that are capable of significantly improving the patient's survival rate when detected early, and provides a method capable of predicting with high reliability the likelihood that primary cancer will progress to metastatic cancer. Thus, the present invention may be useful for efficient prevention or treatment of metastatic cancer by inhibiting the expression of these factors.

The invention relates to the technical field of biological medicines, in particular to application of a ZNF710 gene in preparation of a preparation for hepatocellular carcinoma diagnosis and / or prognosis evaluation. The invention finds and verifies that ZNF710 is closely related to prognosis of liver cancer for the first time and is a key gene for occurrence and development of tumors, and patients with low expression of the ZNF710 gene have higher survival rate. Therefore, compared with the prior art, a novel liver cancer diagnosis and treatment scheme taking the ZNF710 gene as the molecular marker has relatively high clinical application value and potential.

The invention discloses screening and application of an endometrial cancer plasma metabolism marker composition. Based on metabonomics research, a group of metabolic marker compositions with significant difference are screened out by analyzing and comparing metabolites in plasma of endometrial cancer patients and healthy people. By means of the screened markers, early, rapid and accurate detection of endometrial cancer can be achieved, so that treatment measures can be taken in time, and the survival rate and life quality of patients are improved. The metabolic marker obtained by the invention has the advantages of non-invasive detection, high sensitivity and specificity, high throughput analysis, cost effectiveness and early screening. Early discovery and treatment of diseases such as cancers are facilitated, and prognosis is improved. In practical application, excellent performance and huge potential are shown, and a new direction is provided for disease diagnosis and health management in the future.

The application discloses application of a sugar chain marker combination in preparation of a product for diagnosing and predicting prognosis of pancreatic cancer, and the marker combination comprises the following N-glycan chains: NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3 and NA3Fb. The application analyzes the correlation between the change of the sugar chain on the serum glycoprotein and the disease, establishes a prediction model of the sugar chain marker combination to detect the pancreatic cancer, thereby assisting in diagnosing and distinguishing the pancreatic cancer from non-pancreatic cancer, and meanwhile, the prognosis state of the pancreatic cancer patient treatment can be evaluated. The method for detecting the sugar chain marker combination has high sensitivity and high specificity for the pancreatic cancer, can help the pancreatic cancer patient to be found and treated early, and can predict and evaluate the prognosis state of the pancreatic cancer patient, thereby improving the overall cure rate and survival rate of the patient.

The present invention discloses WTAP as a prognosis prediction marker for nasopharyngeal carcinoma, a detection primer and a kit, and relates to the field of molecular biotechnology of nasopharyngeal carcinoma. The present invention uses WTAP as a prognosis prediction marker for recurrent nasopharyngeal carcinoma, develops molecular primers for detecting WTAP expression, detects WTAP expression of recurrent nasopharyngeal carcinoma through molecular primers expressing WTAP, predicts the prognosis of this type, strengthens the post-treatment management or treatment of recurrent NPC patients with poor prognosis, and is expected to increase the 2-year survival of recurrent NPC patients with poor prognosis by 65% ​​and 55% respectively; the present invention is aimed at the population with relatively poor prognosis of recurrent NPC, and through the prediction of the prognosis of this type of patients by this kit, it is expected to significantly increase the 2-year or even 5-year survival rate of such patients.

The application discloses a plasma metabolic marker composition and a screening method and application thereof, and relates to the technical field of biomedicine. The plasma metabolic marker composition comprises glycyl-L-glutamic acid, phosphatidylcholine 38:7, pyrrole-2-formic acid and phosphatidylethanolamine 36:2p. The metabolic marker composition is significantly different in different cancer types, can distinguish different cancer types of cancer patients, and realizes the detection of pan-cancer. The metabolic marker composition is non-invasive and minimally invasive when used for the detection of pan-cancer, is more suitable for the screening and detection of a large number of ordinary people, has high sensitivity and specificity, is more suitable for the early screening of cancer, and is helpful for the early screening, early diagnosis and early treatment of cancer, and is helpful for improving the survival rate and cure rate of cancer patients.

The invention relates to the technical field of biomedicine, in particular to a diagnostic kit based on acute leukemia prognosis and drug sensitivity prediction, which comprises a prediction module I and a prediction module II. The prediction module I is used for predicting the clinical survival rate of a patient and takes one or more of 32 molecules such as CCND3, FERMT3 and PLD4 as prognostic markers; the prediction module II is used for predicting the sensitivity of the body to drugs, and one or more molecules of BMP8B, IGF1R, OTULINL, SLC22A15, CERS1 and PDE4A are used as drug sensitivity markers. By constructing a dual prediction model, the limitation of prediction efficiency of a single biomarker is broken through, functional coupling of survival prognosis and multi-drug sensitivity prediction is realized, and a clinically convertible integrated tool is provided for accurate treatment of AML (acute myeloid leukemia).

The application discloses a method for predicting immunogenic peptide segments of tumor intratumoral microorganisms and application thereof, and mainly comprises the following steps: collecting tumor tissue RNA-seq data, screening microorganism markers related to tumor immunity, and predicting immunogenic peptide segments in microorganisms. The application first proposes a method for predicting immunogenic peptide segments in tumor microorganisms based on sufficient mining of tumor RNA-seq data, so that new targets for tumor immunotherapy are provided, and the possibility of improving the survival rate and prognosis effect of tumor patients is realized.

The present invention discloses colorectal cancer biomarkers and their applications in diagnosis, prevention, treatment, and prognosis. The present invention also discloses distinct single-cell characteristics of blood and intestinal ILCs subsets under healthy conditions and in the setting of colorectal cancer. The healthy intestine is composed of ILC1s, ILC3s, and ILC3s / NK cells, but lacks ILC2s. Additional tumor-specific ILC1s and ILC2s subsets were identified in CRC patients. The present invention also discloses that SLAMF1 is selectively expressed on tumor-specific ILCs, and higher levels of SLAMF1+ ILCs are observed in the blood of CRC patients. The survival rate of rectal cancer patients with high SLAMF1 expression was significantly higher than that of those with low SLAMF1 expression, indicating that SLAMF1 is an anti-tumor biomarker in CRC. The present invention also discloses the use of unsupervised hierarchical clustering methods to investigate the heterogeneity of auxiliary ILCs in blood, normal mucosa, and intestinal tumors during steady state and CRC. These biomarkers can effectively diagnose, prevent, treat, assess prognosis, and predict survival for CRC.

The invention discloses a colorectal cancer multi-target prognosis risk assessment model construction method based on palmitoylation related lncRNA, and relates to the technical field of colorectal cancer prognosis model construction. The method comprises the following steps: step 1, data acquisition; step 2, identifying lncRNA (long non-coding ribonucleic acid) related to S-palmitoylation; step 3, determining a key lncRNA (long non-coding RNA) for constructing a prognosis model; 4, establishing a risk scoring model; and 5, testing the prognosis model. Based on a TCGA database, palmitoylation related genes and CRC transcriptome data are integrated, lncRNA significantly related to the palmitoylation related genes and the CRC transcriptome data is screened through co-expression analysis, and a multi-target prognosis risk assessment model based on the palmitoylation related lncRNA is constructed. And further model testing and mechanism exploration, immune infiltration and mutation load evaluation, drug sensitivity prediction and the like are carried out, so that a more accurate prognosis evaluation tool is expected to be provided for CRC patients, the development of mechanism-driven individualized treatment strategies is promoted, and the method has important significance in improving treatment of the patients and increasing the survival rate.

The application discloses a biomarker related to bladder cancer and application thereof, the biomarker is tRF-1:28-chrM.Ser-TGA and / or tiRNA-1:34-Glu-CTC-1-M2, which can be used for preparing a bladder cancer early high-risk patient diagnosis and / or prognosis evaluation product, and belongs to the technical field of biological medicine. The biomarker is screened through RNA sequencing, has higher sensitivity and specificity as a biomarker, and is verified through qRT-PCR and ROC curve analysis, and it is proved that tRF-1:28-chrM.Ser-TGA and tiRNA-1:34-Glu-CTC-1-M2 can excellently distinguish bladder cancer patients from normal people, and can be used as an early diagnostic biomarker and a new treatment target of bladder cancer patients, which has great clinical value and practical significance for early clinical diagnosis of bladder cancer, related treatment target evaluation and improvement of the survival rate of patients.