21 results about "Good prognosis" patented technology

The invention belongs to the field of biotechnology and medical technology, and discloses application of tumor-associated macrophages with high expression of SLC16A10 in prognosis diagnosis and treatment of colorectal cancer. According to the invention, colorectal cancer single-cell transcriptome sequencing data analysis before and after anti-PD-1 treatment is carried out in the earlier stage; the tumor-associated macrophage subgroup with high expression of the SLC16A10 is enriched in a response group after colorectal cancer anti-PD-1 treatment, and the prognosis of a colorectal cancer patient with high expression of the SLC16A10 is good. Knock-down of the SLC16A10 leads to reduction of expression of the macrophage M1 type marker, and activation and toxicity of co-cultured T cells are reduced. The SLC16A10 promotes T cell activation and weakens immunosuppression on T cells, so that colorectal cancer anti-PD-1 treatment response is caused. The research explains the influence and mechanism of the macrophage SLC16A10 on colorectal cancer anti-PD-1 treatment, and provides a new strategy and theoretical basis for immunotherapy of colorectal cancer.

The invention relates to the technical field of biology, and provides a LanCL1 gene as a target for typing and prognosis of glioma. According to the application of the reagent for detecting LanCL1 gene expression in preparation of products for glioma molecular typing and prognosis prediction, molecular typing of glioma patients can be achieved, LanCL1 high expression is highly related to low malignancy and good prognosis, and clinical risk stratification, treatment decision and treatment assistance are facilitated.

This disclosure presents methods and reagents for cancer prognosis and treatment based on APOE genotyping. Specifically, the presence of the APOE2 genotype is indicative of a poor prognosis for the subject, whereas the presence of the APOE4 genotype is indicative of a good prognosis for the subject. The disclosed methods and reagents are useful for treating cancer and for making medical and clinical decisions on adjuvant therapy, tumor surveillance, and the likelihood of disease progression.

The invention relates to bioinformatics and biomedicine, in particular to a lung cancer patient immunotherapy prognosis prediction model based on proteomics. Metabonomics detection is carried out on peripheral blood of a lung cancer patient, and a PFS prognosis prediction model combining 16 differential proteins is found. The prediction model training set AUC is 0.9349, the sensitivity is 0.7949, the specificity is 1.0000, the accuracy is 0.8769, the NPV is 0.7647, and the PPV is 1.0000; the test set AUC is 0.8190, the sensitivity is 0.8571, the specificity is 0.7333, the accuracy is 0.7931, the NPV is 0.8462, and the PPV is 0.7500. When the output cut off value is larger than or equal to 0.5, the prognosis of the lung cancer immunotherapy patient is good, the progression-free lifetime is longer than 6 months, the prognosis of the lung cancer immunotherapy patient can be accurately predicted through the prediction model, and clinical guidance is provided. Wherein the 16 differential proteins are A6XNE2, P11226, A0A075B6P5, B6EDE2, Q7Z2U7, P33908, A0A0X9T0I7, A0A2U8J8W0, A0A2U8J8Z2, P0DP08, A0A2U8J9C2, A0A120HF66, Q53HT9, Q0ZCI0, A0A140VK43 and A0A0X9V981. The invention also relates to a method for preparing the protein.

The invention discloses an intrahepatic cholangiocarcinoma or tumor conventional ultrasonic image auxiliary diagnosis method and system based on a Vision-RWKV framework, and relates to the technical field of medical image auxiliary diagnosis. Comprising the following steps: acquiring image data information which is conventional liver ultrasonic image data of a plurality of existing patients; constructing an intrahepatic cholangiocarcinoma or tumor identification model based on a Vision-RWKV architecture, wherein the model has a linear complexity bidirectional attention mechanism; training an intrahepatic cholangiocarcinoma or tumor recognition model based on a Vision-RWKV framework by adopting the image data information; and inputting a conventional liver ultrasonic image of a patient to be identified into the trained intrahepatic cholangiocarcinoma or tumor identification model to obtain an identification result, wherein the result comprises a lesion type, lesion position calibration and credibility. The method has the advantages that ultrasonic examination is simple, economical and easy to operate, and CT / MRI examination is high in accuracy, so that more and wider crowds are covered and served, missed diagnosis and misdiagnosis are reduced, and patients obtain good prognosis and life quality.

The invention relates to bioinformatics and biomedicine, in particular to a lung cancer patient immunotherapy prognosis prediction model based on metabonomics. Metabonomics detection is carried out on peripheral blood of a lung cancer patient, and a PFS prognosis prediction model combining 17 differential metabolites is found. A prediction model training set AUC is 0.9497, the sensitivity is 0.9231, the specificity is 0.8846, the accuracy is 0.9077, the NPV is 0.8846, and the PPV is 0.9231; the test set AUC is 0.8810, the sensitivity is 0.9286, the specificity is 0.7333, the accuracy is 0.8276, the NPV is 0.9167, and the PPV is 0.7647. When the output cut off value is greater than or equal to 0.5, the prognosis of the lung cancer immunotherapy patient is good, and no progression lifetime gt exists; after 6 months, the immunotherapy prognosis of the lung cancer patient can be accurately predicted through the prediction model, and clinical guidance is provided. Wherein the 17 differential metabolites are as follows: Com56newg, Com513newg, Com327 newg, Com527newg, Com206newg, Com521newg, Com176newg, Com383newg, Com95newg, Com520newg, Com22newg, Com416newg, Com24newg, Com427newg, Com167newg, Com117newg and Com367newg. The invention also relates to a method for preparing the differential metabolite.

The invention relates to a sepsis diagnosis and treatment scheme recommendation method based on reinforcement learning and clinical knowledge, and the method comprises the steps: firstly modeling an antibiotic combination use problem in a sepsis treatment process into a Markov decision process, and constructing a model through DQN; secondly, deep reinforcement learning based on a value function is combined with sepsis clinical data and medical guideline related content, clinical real data are referred to in a reward function, SOFA scoring knowledge and clinical guideline content are fused, and the model is guided to conduct strategy recommendation in the direction of reasonably shortening antibiotic medication duration and ensuring good prognosis of a patient. According to the constructed model, patient demographic characteristics, basic vital signs, microbial culture results and antibiotic use data serve as model input, four reward functions are constructed in combination with clinical priori knowledge, clinical data and medical guide guidance, the medical interpretability and clinical consistency of SAI-DQN are enhanced, and the method is suitable for clinical application and popularization. And the SAI-DQN is utilized to provide personalized antibiotic combined treatment suggestions for the sepsis patients.

The invention relates to a molecular marker for diagnosis, prognosis evaluation and treatment of cardiac fibrosis, and belongs to the technical field of biological medicines. The invention provides a molecular marker capable of diagnosing cardiac fibrosis, evaluating the severity of cardiac fibrosis, evaluating the prognosis of cardiovascular diseases accompanied with cardiac fibrosis or evaluating the prognosis of acute myocardial infarction. The molecular marker comprises a hepatocyte growth factor. Research finds that the HGF is remarkably increased in plasma of a patient with cardiac fibrosis, and the plasma HGF level of a patient with aortic valve stenosis is also remarkably increased before an operation, so that the HGF can be used for diagnosing cardiac fibrosis and evaluating the severity of cardiac fibrosis. Researches find that timely transient increase of the HGF in plasma of a patient with acute myocardial infarction is significantly related to good prognosis, and continuous low-level increase, continuous high-level increase, recurrence and delayed increase of the HGF are significantly related to poor prognosis, so that the HGF can be used for evaluating prognosis of acute myocardial infarction.

The invention provides a non-functional pancreatic neuroendocrine tumor prognostic marker and application thereof. The prognostic marker is one or more of GNAO1, INA and VCAN. Wherein the high expression of GNAO1 and INA is related to good prognosis, and the high expression of VCAN is related to poor prognosis. Three prognostic related proteins are combined into a multivariable prognostic marker, and the multivariable prognostic marker has excellent performance in the aspect of distinguishing high-risk patients from low-risk patients and has a good calibration effect in the aspect of estimating absolute risks, and the prediction error rate is low.

The present invention relates to a method for assessing disease severity or a clinical stage of, or predicting prognosis for chemotherapy in, dogs suffering from lymphoma. According to the present invention, in dogs suffering from lymphoma, it has been confirmed that CD34-positive expression is associated with a higher risk of cranial mediastinal lymph node metastasis and fever compared to CD34-negative expression; and since the CD34-positive expression is classifiable as a clinical disease within the WHO clinical substages, CD34 positivity can serve as an indicator of poor prognosis. In addition, it has been confirmed that MHC class II (MHCII)-positive expression is associated with a higher risk of fever and adverse effects from chemotherapy compared to MHCII-negative expression, and thus can serve as an indicator of favorable prognosis. Accordingly, the present invention may contribute not only to preserving companion dog health and reducing medical expenses for pet owners, but also to improving the quality of veterinary consultations.

The present application relates to the technical field of medical diagnosis, and particularly relates to the use of a detection reagent of CROT protein in a product for prognosis of nasopharyngeal carcinoma risk, wherein the amino acid sequence of the CROT protein is shown as SEQ ID NO. 1. The subject for prognosis of nasopharyngeal carcinoma risk is a human, and the sample for detection is a tissue of the subject. The high expression of the CROT protein is related to the occurrence and malignant growth behavior of nasopharyngeal carcinoma, and compared with a healthy control, the high expression of the CROT protein indicates that the subject has nasopharyngeal carcinoma or has the risk of nasopharyngeal carcinoma; the expression of the CROT protein in a tissue of a nasopharyngeal carcinoma patient is increased, and it is determined as a poor prognosis; the expression of the CROT protein in a tissue of a nasopharyngeal carcinoma patient is decreased, and it is determined as a good prognosis.

The application provides a non-functional pancreatic neuroendocrine tumor prognostic marker and application thereof, the prognostic marker is one or more of GNAO1, INA and VCAN;Wherein GNAO1 and INA high expression is related to good prognosis, and VCAN high expression is related to poor prognosis.Three prognostic related proteins are combined into a multivariate prognostic marker, the multivariate prognostic marker is excellent in distinguishing high-risk patients and low-risk patients, and shows good calibration effect in estimating absolute risk, and the prediction error rate is low.

The present invention discloses a method, device, electronic device and storage medium for predicting the prognosis of meningeal carcinoma derived from lung adenocarcinoma, belonging to the field of intelligent prediction methods. The method comprises: obtaining genetic information of a cerebrospinal fluid sample from a patient with meningeal carcinoma derived from lung adenocarcinoma; performing feature extraction on the genetic information to obtain genetic characteristics and content data thereof, wherein the genetic characteristics are IL-6 and / or TNF-α; performing classification prediction based on the content data of the genetic characteristics to obtain a classification result of whether the sample has a good prognosis or a poor prognosis. The present invention achieves a more accurate prognosis prediction effect for meningeal carcinoma derived from lung adenocarcinoma, providing a basis for subsequent clinical diagnosis and treatment.

The invention belongs to the technical field of biology, and particularly relates to application of lymphotoxin beta in preparation of a medicine for treating triple-negative breast cancer, triple-negative breast cancer patients in a high immune infiltration group are screened out by adopting unsupervised clustering analysis, and remarkable good prognosis is shown. Five key genes (LTB, IRF8, IDO1, ITM2A and LCP1) for determining prognosis of triple negative breast cancer patients in a high immune infiltration group are screened out, and a corresponding triple negative breast cancer prognosis prediction model is constructed; through research on immune infiltration key gene lymphotoxin beta (LTB), LTB expression is screened out to be a key gene for determining prognosis of TNBC patients, related biological functions of LTB participating in enhancement of killing activity of T cells are clarified, a key role of immune infiltration in prognosis of the TNBC patients is clarified, and an important basis and a potential target are provided for improvement of TNBC immunotherapy effects.

The present invention belongs to the field of biotechnology, and specifically relates to a group of urine protein markers and their use in preparing a product for predicting the prognosis of non-small cell lung cancer. The present invention screens out for the first time from the urine of patients with non-small cell lung cancer a group of urine proteins closely related to the postoperative progression of non-small cell lung cancer: AVIL protein, CXCR5 protein, ENPEP protein, DPEP1 protein, SNC66 protein and XPNPEP2 protein, and constructs a prognosis prediction model based on these six urine proteins. The constructed prognosis prediction model has been verified to have high sensitivity and specificity for predicting the prognosis of patients after surgery of non-small cell lung cancer, reaching 84.5% and 81.2% respectively. It has a good prognosis prediction effect for non-small cell lung cancer, provides reliable guidance for clinical targeted treatment, and is of great significance for improving the prognosis and survival of patients with non-small cell lung cancer.

The present invention relates to the field of medical diagnostic technology, and more specifically to the use of a detection reagent for VAPA protein in the preparation of a product for diagnosing and prognosing the risk of colon cancer. The amino acid sequence of the VAPA protein is shown in SEQ ID NO.1, the subject of diagnosis and prognosis is a human, and the sample for detection is the tissue of the subject. The low expression of the VAPA protein described in the present invention is associated with the occurrence and malignant growth behavior of colon cancer. Compared with healthy controls, low expression of the VAPA protein indicates that the subject has colon cancer or is at risk of developing colon cancer; the expression of VAPA protein in tissue samples of colon cancer patients is reduced, which is judged as a poor prognosis; the expression of VAPA protein in tissue samples of colon cancer patients is increased, which is judged as a good prognosis. The results suggest that VAPA protein is a potential target for the treatment of colon cancer.

The application discloses a system for predicting the prognosis of a liver cancer patient based on HED. The system comprises a sample data acquisition module, an HED calculation module, a tumor-related antigen peptide prediction module and a prediction result output module. The system for predicting the prognosis of a liver cancer patient based on HED can more accurately predict the prognosis of liver cancer. It is found that high HED is related to good prognosis of liver cancer, corresponding to more TAA peptide segments capable of binding to HLA-I and stronger anti-tumor T cell response, and the above principle can be applied to predict the recurrence risk of early, medium and late liver cancer patients after treatment.

The invention relates to the technical field of molecular diagnosis, in particular to a group of PD1 / PD-L1 prognostic biomarkers for tumor treatment and application of the PD1 / PD-L1 prognostic biomarkers. The biomarker disclosed by the invention is simple and convenient to sample, high in accuracy and quick in result reporting. When the expression level of Pseudomonas viridiflava is up-regulated, the prognosis of PD1 / PD-L1 in treating tumors is good, the AUC value (threshold value 0.04386) for predicting the curative effect of immunotherapy in a screening set is 0.9063, and the AUC value (threshold value 0.05311) for predicting the curative effect of immunotherapy in a verification set is 0.8056. When the expression level of Rothiia mucilaginosa is reduced, the prognosis of the PD1 / PD-L1 for treating the tumor is poor. The AUC value for predicting the curative effect of the immunotherapy in the screening set is 0.8563 (the threshold value is 0.06214), and the AUC value for predicting the curative effect of the immunotherapy in the verification set is 0.7111 (the threshold value is 0.06690). The method has important scientific significance and clinical value in improving individualized precise treatment application of immunotherapy, provides an effective molecular target for individualized precise treatment of lung cancer patients, provides a basis for a new intervention strategy, and provides more reliable lung cancer diagnosis and prognosis prediction.

The present invention belongs to the field of disease diagnosis and treatment and molecular biology technology, and specifically relates to the application of circRNA in enhancing the chemotherapy sensitivity of triple-negative breast cancer and the prognosis assessment of breast cancer patients. Through research, the present invention found that the expression of circRNA-CREIT in breast cancer tissue was significantly lower than that in the surrounding normal breast tissue, and the expression in TNBC cell lines was significantly lower than that in non-TNBC, and circRNA-CREIT overexpression was significantly correlated with a good prognosis in breast cancer patients. Overexpression of circRNA-CREIT in TNBC cell lines can significantly enhance the sensitivity of cells to the chemotherapy drug doxorubicin. Therefore, circRNA-CREIT can provide a new therapeutic target for enhancing the chemotherapy sensitivity of TNBC and provide a new predictive factor for the prognosis judgment of breast cancer patients, and therefore has good practical application value.

The invention provides a kit for prognosis judgment of sepsis and application of the kit. The kit comprises a CD45 / CD14 / CD16 / HLA-DR / VSIG4 / IL1R2 antibody which is used for multicolor flow cytometry detection and is provided with a fluorescent label. A mononuclear cell is expressed and circled according to CD45 / CD14 / CD16, and a VSIG4 + HLA-DRlowCD14 + mononuclear cell (a cell proportion P1) and an IL1R2 + HLA-DRlowCD14 + mononuclear cell (a cell proportion P2) are expressed and circled according to HLA-DR / VSIG4 / IL1R2 in the mononuclear cell. When P1gt; 20% or P2gt; if the content is 35%, the sepsis patient is poor in prognosis; when P1lt; 20% and P2lt; if the percentage is 35%, the sepsis patient has good prognosis. The kit is high in prognosis sensitivity and specificity for distinguishing different sepsis patients, and high-risk patients can be rapidly distinguished.

The present invention relates to the field of molecular biology and biomedicine, and more particularly to a myeloma biomarker and its application. According to the above-mentioned application of the present invention, by detecting the expression level of the SERPINF2 gene or SERPINF2 protein in a subject, it is possible to determine whether the subject has myeloma, whether the subject is at risk of developing myeloma, or whether the subject has a good prognosis, thereby guiding clinicians to provide effective preventive measures or treatment plans for the subject, which is conducive to improving the subject's survival rate.