New approach to generating insights for aging research based on literature mining and knowledge integration

An Evaluation Model for the Influence Factors of Interest in Literature Courses Based on Data Analysis and Association Rules in a Small-Sample Environment

The primary tools for developing pupils' creativity, capacity for verbal expression, and spiritual growth are literary reading and writing. Literature is a sort of art that elicits feelings and expresses the author's comprehension and outlook on social life via the use of language. Reading and writing literary works helps students develop their aesthetic sensibilities and capacity to create compelling images, as well as their spirituality and wisdom. This study suggests a data mining-based optimal design approach for analyzing association rules of influencing aspects of interest in literary courses. To increase the frequency and accuracy of data mining, association rules are used to obtain the association mapping relationship between data sets of influencing factors of interest in literature courses. Rough set theory is then used to distinguish between the feature sets of data sets in the same subspace and different subspaces. To identify the most prevalent factor that affects the interest of the curriculum's literary components and then to conduct simulation testing and analysis. The proposed arithmetic has a particular accuracy, which is 8.25% greater than the conventional arithmetic, according to simulation findings. This outcome demonstrates in full how the enhanced arithmetic decreases the amount of records in the scanning database by grouping and compressing the database, hence lowering the scanning time and pruning before the connection process of Liu arithmetic. Classical literary works are without a doubt the most valuable resources to feed, edify, forge, and grow the spirit, soul, and personality of contemporary individuals throughout history and in all nations. Education through literature develops one's character, spirit, emotions, and aesthetic sense. The importance of reaffirming the significant place of literature education in contemporary national basic education cannot be overstated.

A novel method of literature mining to identify candidate COVID-19 drugs

Summary

COVID-19 is a serious infectious disease that has recently emerged and continues to spread worldwide. Its spreading rate is too high to expect that new specific drugs will be developed in sufficient time. As an alternative, drugs already developed for other diseases have been tested for use in the treatment of COVID-19 (drug repositioning). However, to select candidate drugs from a large number of compounds, numerous inhibition assays involving viral infection of cultured cells are required. For efficiency, it would be useful to narrow the list of candidates down using logical considerations prior to performing these assays. We have developed a powerful tool to predict candidate drugs for the treatment of COVID-19 and other diseases. This tool is based on the concatenation of events/substances, each of which is linked to a KEGG (Kyoto Encyclopedia of Genes and Genomes) code based on a relationship obtained from text mining of the vast literature in the PubMed database. By analyzing 21 589 326 records with abstracts from PubMed, 98 556 KEGG codes with NAME/DEFINITION fields were connected. Among them, 9799 KEGG drug codes were connected to COVID-19, of which 7492 codes had no direct connection to COVID-19. Although this report focuses on COVID-19, the program developed here can be applied to other infectious diseases and used to quickly identify drug candidates when new infectious diseases appear in the future.

Availability and implementation

The programs and data underlying this article will be shared on reasonable request to the corresponding authors.

Contact

atmuramatsu@g.ecc.u-tokyo.ac.jp, amtanok@mail.ecc.u-tokyo.ac.jp.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Cardiac ageing: extrinsic and intrinsic factors in cellular renewal and senescence

Cardiac ageing manifests as a decline in function leading to heart failure. At the cellular level, ageing entails decreased replicative capacity and dysregulation of cellular processes in myocardial and nonmyocyte cells. Various extrinsic parameters, such as lifestyle and environment, integrate important signalling pathways, such as those involving inflammation and oxidative stress, with intrinsic molecular mechanisms underlying resistance versus progression to cellular senescence. Mitigation of cardiac functional decline in an ageing organism requires the activation of enhanced maintenance and reparative capacity, thereby overcoming inherent endogenous limitations to retaining a youthful phenotype. Deciphering the molecular mechanisms underlying dysregulation of cellular function and renewal reveals potential interventional targets to attenuate degenerative processes at the cellular and systemic levels to improve quality of life for our ageing population. In this Review, we discuss the roles of extrinsic and intrinsic factors in cardiac ageing. Animal models of cardiac ageing are summarized, followed by an overview of the current and possible future treatments to mitigate the deleterious effects of cardiac ageing.