ABSTRACT
[This corrects the article DOI: 10.1016/j.scr.2023.103115.].
ABSTRACT
Recent advances in RNA engineering have enabled the development of RNA-based therapeutics for a broad spectrum of applications. Developing RNA therapeutics start with targeted RNA screening and move to the drug design and optimization. However, existing target screening tools ignore noncoding RNAs and their disease-relevant regulatory relationships. And designing therapeutic RNAs encounters high computational complexity of multi-objective optimization to overcome the immunogenicity, instability and inefficient translational production. To unlock the therapeutic potential of noncoding RNAs and enable one-stop screening and design of therapeutic RNAs, we have built the platform TREAT. It incorporates 43,087,953 regulatory relationships between coding and noncoding genes from 81 biological networks under different physiological conditions. TREAT introduces graph representation learning with Random Walk Diffusions to perform disease-relevant target screening, in addition to the commonly utilized Topological Degree and PageRank algorithms. Design and optimization of large RNAs or interfering RNAs are both available. To reduce the computational complexity of multi-objective optimization for large RNA, we stratified the features into local and global features. The local features are evaluated on the fixed-length or dynamic-length local bins, whereas the latter are inspired by AI language models of protein sequence. Then the global assessment is performed on refined candidates, thus reducing the enormous search space. Overall, TREAT is a one-stop platform for the screening and designing of therapeutic RNAs, with particular attention to noncoding RNAs and cutting-edge AI technology embedded, leading the progress of innovative therapeutics for challenging diseases. TREAT is freely accessible at https://rna.org.cn/treat.
ABSTRACT
OBJECTIVE:To provide reference for improving rational drug use in undergraduates from non-medical college. METHODS:By questionnaire survey,9 non-medical colleges were collected according to college entrance examination admission batches and school types stratification sampling. The questionnaires were issued among 860 college students by encounter sampling. The survey included medication knowledge,medication behavior,personal basic information three aspects. Single factor analysis and Logistic regression analysis were conducted for their influential factors. RESULTS:A total of 860 questionnaires were sent out, and 812 valid questionnaires were collected with effective recovery rate of 94.4%. In respect of medication knowledge,4.2% of college students answered all of the five questions correctly,the correlation of correct rate with monthly disposable income was maximal(P=0.007);correct rate of the minority students with monthly disposable income more than 6 000 yuan was higher. In respect of medication behavior,14.4% of college students answered all of the seven questions correctly,the correlation of correct rate with whether the family had long-term(more than half a year)medication experience was maximal(P=0.035);the students whose family had no long-term(more than half a year)medication experience member and had medical personnel used drugs more standardly. CONCLUSIONS:The non-medical college students in Beijing lack of medication knowledge and have poor compliance;medication behavior is also unreasonable. Society,universities and families should strengthen the health education of students,and guide students to standardize the self-medicine therapy.
