3results about How to "Confirm the accuracy" patented technology

This invention provides an endoplasmic reticulum-targeted single-chain cyclic poly(β-amino ester), its preparation method, and its applications. The structural formula of the endoplasmic reticulum-targeted single-chain cyclic poly(β-amino ester) is as follows. The endoplasmic reticulum-targeted single-chain cyclic poly(β-amino ester) exhibits excellent affinity for DNA, and the complex nanoparticles formed with DNA are uniformly distributed, stable, biodegradable, and biocompatible. The endoplasmic reticulum-targeted single-chain cyclic poly(β-amino ester) and DNA can be efficiently taken up by cells, exhibiting excellent endoplasmic reticulum targeting performance. It can overcome the limitations of gene retention in lysosomes and nuclear internalization of the complex nanoparticles, enabling efficient delivery of DNA and mRNA.

The invention provides a D-pantothenic acid fermentation multi-target dynamic optimization method based on biological-digital twinning, which comprises the following steps: constructing a biological soft sensor through multiple machine learning model algorithms, and combining the constructed biological soft sensor with a neural network. Meanwhile, fermentation process conditions are optimized by applying a particle swarm algorithm and a genetic algorithm, so that the whole fermentation process simulation monitoring under data driving is realized, the problems of insufficient accuracy and poor robustness when a traditional prediction technology is used for processing complex fermentation data are effectively solved, and the predictability and controllability of the fermentation process are improved.

The present application belongs to the technical field of molecular medicine, and relates to a probe, a marker combination and a prediction method for non-invasive risk grading of neuroblastoma. The probe UUU-DZ-tFNA can detect UDG in vitro, cells, living bodies and plasma exosomes, and it is proved that UDG in plasma exosomes is closely related to the risk grading of NB. The plasma exosomes of NB patients are collected MYCN Whether to amplify, whether the tumor is metastatic, neuron-specific enolase content, lactate dehydrogenase content and plasma exosome UDG content are integrated, and a NB non-invasive risk grading prediction model is established through a machine learning algorithm. The optimal risk grading model is a neural network model. The area under the receiver operating characteristic curve of the combined model is improved by 5.2% compared with the optimal prediction result of the combined prediction model of the single clinical index, and the sensitivity and specificity are significantly improved. The present application has key clinical values for accurate grading of NB children, individualized treatment plan formulation and efficacy monitoring.