9 results about "Good prognosis" patented technology

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.

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 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 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 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.