7 results about "Advanced stage" patented technology

The application relates to the technical field of biological medicine, and discloses siRNA for inhibiting a P311 gene, a composition and application thereof. The siRNA can specifically knock down P311 gene expression in human tumor cells, can be used as an auxiliary component for improving the drug efficacy of gemcitabine, can improve the drug sensitivity of a pancreatic cancer patient to gemcitabine, can reduce the gemcitabine drug resistance of a pancreatic cancer patient in an advanced stage, can improve the prognosis of the pancreatic cancer patient, can prolong the survival period of the pancreatic cancer patient in the advanced stage, and has important significance for the treatment of pancreatic cancer.

Aspects of the disclosure provide a formulation for treating advanced pancreatic cancer, comprising a combination of a cannabinoid, a terpene, and a flavonoid. To enhance bioavailability and targeted delivery, the formulation is incorporated into a nanoplatform, such as a nanoemulsion, optimizing absorption and delivery to cancer cells. The nanoparticle-based composition specifically targets advanced stages of pancreatic cancer, aiming to inhibit tumor progression and improve clinical outcomes.

The present invention concerns a new gene therapy approach to increase light-sensitivity in degenerating cones in advanced stages of rod-cone dystrophy (RCD) mediated by G-protein-gated-K+ channel (GIRK), in particular GIRK1 F137S, activated by G proteins recruited by cone opsin expressed in degenerating cones.

The present invention consists in combining low dose intestinal radiation (ILDR) with immunotherapy or other antineoplastic treatments, in the ideal context of intestinal presence or prevalence of Christensenellaceae family members, especially for solid cancers at an advanced stage (refractory to first line chemo or immunotherapy). The invention also pertains to a bacterial composition,e.g. comprising Chistensenella bacteria, for use in the treatment of cancer in a patient, in combination with intestinal low dose radiotherapy (ILDR) and an antineoplastic treatment.

The invention relates to panels of biomarkers including proteins phosphatase 1 regulatory subunit 14A and / or 2′,3′-cyclic-nucleotide 3′-phosphodiesterase and / or phosphorylated tau or fragments thereof and methods using thereof for diagnosing, staging, treating and assessing the response of a treatment for a neurocognitive disorder characterised by tau toxicity, in particular for Alzheimer's disease. The present invention shows that the biomarkers disclosed herein are elevated in the brain of subjects with an advanced stage of a neurocognitive disorder (Braak stage V / VI) and / or are regulated in the CSF of AD subjects in comparison to cognitively affected non-AD controls; and / or regulated in response to two casein kinase 1 delta inhibitors.

The present invention relates to a pharmaceutical composition comprising a compound selected from the group consisting of elafibranor, 2-[2,6-dimethyl-4-[3-[4-(methylthio)phenyl]-3-oxo- propyl]phenoxy]-2-methylpropanoic acid (GFT1007), and a pharmaceutically acceptable salt of elafibranor or GFT1007, for use in a method for treating primary biliary cholangitis (PBC) in a subject having advanced stage primary biliary cholangitis (PBC).

This application discloses a method and related equipment for predicting the efficacy of immunotherapy in advanced lung cancer, belonging to the field of deep learning technology. The method includes: acquiring transcriptome sequencing data of the user to be predicted; inputting the transcriptome sequencing data into an immunotherapy efficacy prediction model to obtain an immunotherapy efficacy score output by the model; and determining the predicted immunotherapy efficacy result for the user based on the immunotherapy efficacy score output by the model. The immunotherapy efficacy prediction model is constructed based on the Cox proportional hazards model ridge regression algorithm, the random survival forest algorithm, and the extreme gradient boosting tree algorithm. This application improves the performance and stability of the prediction model by combining these algorithms, thereby enhancing the accuracy of predicting the efficacy of immunotherapy in advanced lung cancer.