6 results about "Disease prognosis" patented technology

Lung cancer remains the leading cause of cancer-related deaths in the world. Despite the fact that introduction of immune checkpoint inhibitors (ICIs) led to a major advancement in lung cancer treatment, disease prognosis continues to remain low and a significant proportion of patients do not respond to such therapies. Cancer vaccines could potentially provide a complementary approach to boost antitumor immunity and act synergistically with ICIs. Novel tumor antigens shared by a large proportion of lung tumor cells are described herein. Several of the tumor antigens described herein derive from aberrantly expressed unmutated genomic sequences, such as intronic and intergenic sequences, which are not expressed in normal tissues. Nucleic acids, compositions, cells and vaccines derived from these tumor antigens are described. The use of the tumor antigens, nucleic acids, compositions, cells and vaccines for the treatment of lung cancer is also described.

This invention relates to the field of image analysis technology, specifically to a method for prognostic assessment of breast and thyroid diseases combined with AI analysis of pathological images. The method includes the following steps: acquiring pathological slide images and performing grayscale statistics to generate tissue coordinate data; calculating the edge gradient and contour features of pixel neighborhoods; quantifying the morphological aberration features of cell nuclei and constructing a nuclear population potential field; analyzing the potential difference and gradient inversion regions; correcting directional consistency based on the gradient magnitude distribution direction; and finally generating a disease prognostic assessment result. In this invention, stable tissues are screened and coordinate data is generated through grayscale statistics and threshold segmentation of pathological slide images; edge gradients are calculated and closed contours are extracted; cell nucleus axis ratios and contour roughness are quantified; a regular grid is established and distance attenuation is introduced to form a nuclear population potential field; and the potential difference and gradient inversion regions are statistically analyzed and directional consistency is corrected, thereby improving the stability and consistency of the prognostic assessment.

A federated learning system and method among medical institutions, and a disease prognosis prediction system including the same. The federated learning system and method are configured to apply a hierarchical clustering-based learning method during federated learning using medical data, and to transmit weights generated based on machine learning results by applying quantum cryptography and timestamp-based encryption techniques. The system and method enable resolution of data heterogeneity among medical institutions, thereby improving the performance of the learning model, and ensures stability by providing protection of personal medical data.

This invention belongs to the fields of disease prognosis and biomedical technology, specifically relating to the application of ZAG in the prognosis of acute-on-chronic liver failure (ACLF). This application specifically discloses the application of reagents for detecting ZAG in the preparation of diagnostic and / or prognostic assessment kits for ACLF. This invention provides reagents for detecting ZAG that can be used for the prognosis of ACLF. Through absolute quantification of ZAG levels in blood samples from different groups of ACLF patients using ELISA technology, as well as the detection of dynamic absolute quantification of ZAG levels, timely detection of ACLF phenomena and disease progression, and the detection of other relevant indicators, thereby providing scientific guidance for the management and decision-making of ACLF using ZAG.

A method for predicting the prognosis of a target disease based on medical images and non-image data according to one embodiment of the present invention may include the steps of: acquiring medical images of a patient in which abnormal areas are to be detected; an image data analysis step of classifying the target disease in the medical images into pre-set groups using a first artificial intelligence model; a first non-image data generation step of acquiring and quantifying the patient's medical data; a second non-image data generation step of quantifying target disease-related features in the medical images; and an integrated analysis step of applying the result values ​​from the image data analysis step, the first and second non-image data to a second artificial intelligence model to predict the prognosis of the target disease.

The application provides a use of an NR2F2 agonist levomilnacipran in preparation of a drug for treating female renal clear cell carcinoma (ccRCC). In an estrogen environment, the levomilnacipran can significantly promote expression of NR2F2, and significantly inhibit proliferation, invasion and migration of ccRCC cells. The levomilnacipran can be used as a novel therapeutic drug for female ccRCC, and can specifically inhibit progression of the female ccRCC, and has important significance for disease prognosis.