64 results about "Observed Survival" patented technology

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 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 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 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 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 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 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.

The invention discloses a prognostic marker combination for colorectal cancer and application thereof, and belongs to the technical field of biological medicine. According to the present invention, the research results show that the expression of the six characteristic genes such as LUM, DCN, SFRP2, CCDC80, SFRP4 and GREM1 is closely related to the survival rate of the colorectal cancer patient; therefore, the prognosis prediction of the colorectal cancer patient can be realized by detecting the expression conditions of the six characteristic genes, so that the survival rate of the colorectal cancer patient can be improved, a new thought is provided for clinical precise treatment decision of the colorectal cancer, and the gene has a good clinical application value.

The invention provides a molecular marker combination based on circRNA (Ribonucleic Acid), wherein the molecular marker combination comprises hsacirc0005986 and hsacirc0067531. The invention also provides application of the molecular marker composition in diagnosis of liver cancer, and a diagnostic kit prepared based on the molecular marker composition. The invention has outstanding advantages in the aspect of auxiliary diagnosis of liver cancer, can realize auxiliary diagnosis of early liver cancer, and improves the survival rate of patients.

The invention provides a culture medium and a culture method of normal oral fibroblasts. The culture medium comprises a DMEM (Dulbecco's Modified Eagle Medium) and the following components based on the volume of the DMEM: 10v% of Australian fetal calf serum, 2.5 ng / ml of bFGF (Basic Fibroblast Growth Factor), 1v% of a penicillin-streptomycin solution, 2.0 ug / ml of hydrocortisone and 0.05 ug / ml of FGF10 (Fibroblast Growth Factor 10). The invention solves the problems of high NF culture difficulty, easy pollution, low survival rate, incapability of permanent passage and the like, provides a basis for further exploring a mechanism for activating the NF into CAFs, is beneficial to finding and finding new therapeutic targets, and provides a new clinical diagnosis and treatment idea for treating and preventing the deterioration of the salivary gland benign tumor.

The invention discloses a multi-parameter conjoint analysis method for early cancer screening and application of the multi-parameter conjoint analysis method, and relates to the technical field of cancer screening. The method comprises the following steps: firstly, acquiring a biological sample of a subject, wherein the biological sample comprises at least one of blood, urine and a tissue biopsy sample; then, the obtained biological sample is pretreated, and impurities in the sample are removed; according to the multi-parameter conjoint analysis method for early cancer screening, multiple biological samples are collected, multiple biological parameters are obtained through multiple detection technologies, and the accuracy and reliability of early cancer screening are improved; compared with the prior art, limitation of single parameter detection is avoided, the defects of a traditional multi-parameter joint analysis method are overcome, and whether a subject has early cancer lesion or not can be more accurately judged in practical application. The application of the method can realize early discovery and intervention of cancers, improves the survival rate of cancer patients, and has important clinical application value and social significance.

The application discloses a multimodal survival prediction method and device, applied to the technical field of data processing, and the method comprises the steps of obtaining pathological images and genomic data of a cancer patient; using a preset survival prediction model to perform survival prediction on the pathological images and the genomic data, and obtaining the survival rate of the cancer patient in a future time period; in the prediction model, an isolated processing structure extracts features from the pathological images and the genomic data to obtain pathological isolated feature sequences and genomic isolated feature sequences, an interaction module interacts the two isolated feature sequences to obtain pathological interaction feature sequences and genomic interaction feature sequences, a cooperative processing structure extracts features from the two interaction feature sequences to obtain pathological cooperative feature sequences and genomic cooperative feature sequences, and a fusion prediction module performs fusion type survival prediction based on the two cooperative feature sequences to obtain the survival rate of the cancer patient in the future time period. The application improves the accuracy of survival prediction.

Provided are a differentially methylated region combination and a use. Provided are 13 differentially methylated regions for diagnosing or assisting in diagnosing cancers. Provided is an accurate, convenient, and economical means for early screening of pancreatic cancer, which can increase the detection rate of pancreatic cancer, especially early pancreatic cancer, in populations at high risk of pancreatic cancer and populations receiving general physical examinations, thereby increasing the survival rate of patients with pancreatic cancer, saving the medical expenditure, and reducing the medical burden.

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 invention relates to the technical field of biological medicine, in particular to breast cancer public new antigen prediction and application thereof. The invention provides a method for predicting the public immunogenic peptide fragment in different molecular subtypes of breast tumors based on full mining of tumor DNA data for the first time, so that a new public target spot is provided for breast cancer immunotherapy, and the survival rate and prognosis effect possibility of breast cancer patients are improved.

The invention discloses a drug screening method based on a comprehensive survival index and application thereof, and belongs to the technical field of drug screening, and the drug screening method comprises the following steps: (1) constructing a disease experimental animal model, and obtaining the survival time, the survival rate, the tumor quality, the net body quality and the pulmonary nodule number of an experimental animal after the experimental animal is administered with a drug; (2) acquiring area data on a net body mass change rate curve according to the net body mass data; acquiring tumor control coefficient data according to the tumor quality and the net body quality; and (3) standardizing the data of the survival rate, the survival time, the area on the net body mass change rate curve and the number of pulmonary nodules by using a summation normalization method. The method provided by the invention can accurately judge the treatment effect of each medicine on diseases and the protection condition of each medicine on body health, objectively reflect the progress condition and prognosis effect of the diseases, screen out the medicine with a good comprehensive treatment effect, and provide more accurate data support for clinical and experimental research.