2results about How to "Level up" patented technology

This invention relates to the field of organoid disease models, and discloses a method for constructing a human brain organoid model of early-onset Alzheimer's disease (AD) and its applications. This invention introduces [a specific technology] into human embryonic stem cell lines through single-base editing and lead editing techniques. PSEN1 ΔE9、 PSEN1 M146V and APP Human embryonic stem cell lines carrying four familial pathogenic gene mutations (K670N and M671L) were constructed and induced to differentiate into Alzheimer's disease (AD) brain organoids. The AD brain organoid model established by this invention exhibited tau phosphorylation pathological phenotypes as early as 20 days and Aβ-related phenotypes as early as 40 days, with increased total Aβ, decreased Aβ42 / Aβ40 ratio, and simultaneous aggravation of Aβ-Tau pathology. This represents a complex neurodegenerative pathological model where multiple mutations synergistically regulate Aβ production and tau phosphorylation, which is highly valuable for understanding and studying early-onset familial AD. This invention provides a human brain organoid model for in vitro AD studies and offers a tool for studying the pathological mechanisms of AD and screening drugs.

The invention relates to the technical field of intelligent sports equipment management, and discloses a sports fitness equipment management method based on an artificial intelligence algorithm. According to the method, a static description text and a dynamic use log of equipment are fused to generate a mixed information stream; mode slicing is carried out on the equipment, and structural feature slices and functional feature slices of the equipment are separated out; respectively mining a static topological relation network and capturing a dynamic behavior track sequence, and carrying out space-time alignment to form a joint representation vector; a relational graph model with the equipment as the center is constructed, nodes are equipment entities, and edges comprise static and dynamic association; and traversing edges in the model, identifying and aggregating edge clusters with the same association mode, and abstracting each edge cluster into a management dimension, thereby automatically constructing a multi-dimensional management framework. According to the method, the management dimension is automatically found from the multi-source data, and the intelligent level of management is improved.