: Aging is an unavoidable process, leads to cell senescence due to physiochemical changes in an organism. Anti-aging remedies have always been of great interest since ancient times. The purpose of anti-aging activities is to increase the life span and the quality of life. Anti-aging activities are primarily involved in the therapies of age-related disorders such as Parkinson's disease (PD), Alzheimer's disease (AD), cardiovascular diseases, cancer and chronic obstructive pulmonary diseases. These diseases are triggered by multiple factors that are involved in numerous molecular pathways including telomere shortening, NF-κB pathway, adiponectin receptor pathway, insulin and IGF signaling pathway, AMPK, mTOR and mitochondria dysfunction. Natural products are known as effective molecules to delay the aging process through influencing metabolic pathways and thus ensure an extended lifespan. These natural compounds are being utilized in drug design and development through computational and high throughput techniques for effective pro-longevity drugs. A comprehensive study of natural compounds demonstrated with their anti-aging activities along with databases of natural products for drug designing was executed and summarized in this review article.

Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modeling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students’ performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performance and identify the critical areas that need significant improvement.