Fruits of Terminalia chebula Retz. (Combretaceae) are widely used as crude drugs in various traditional medicine systems. The aim of this article is to review the available scientific information regarding the traditional uses, bioactive chemical constituents and the pharmacological activities of T. chebula. Numerous researches conducted on T. chebula have confirmed the presence of wide range of the phytochemicals such as flavonoids, tannins, phenolic acids and other bioactive compounds. T. chebula is also widely studied regarding its pharmacological activities such as antioxidant, hepatoprotective, neuroprotective, cytotoxic, antidiabetic, anti-inflammatory activities among others. However, more in vivo and clinical studies for mechanism-based pharmacological evaluation should be conducted in future to provide stronger scientific evidences for their traditional uses.
As big data becomes ubiquitous across domains, and more and more stakeholders aspire to make the most of their data, demand for machine learning tools has spurred researchers to explore the possibilities of automated machine learning (AutoML). AutoML tools aim to make machine learning accessible for non-machine learning experts (domain experts), to improve the efficiency of machine learning, and to accelerate machine learning research. But although automation and efficiency are among AutoML’s main selling points, the process still requires human involvement at a number of vital steps, including understanding the attributes of domain-specific data, defining prediction problems, creating a suitable training dataset, and selecting a promising machine learning technique. These steps often require a prolonged back-and-forth that makes this process inefficient for domain experts and data scientists alike and keeps so-called AutoML systems from being truly automatic. In this review article, we introduce a new classification system for AutoML systems, using a seven-tiered schematic to distinguish these systems based on their level of autonomy. We begin by describing what an end-to-end machine learning pipeline actually looks like, and which subtasks of the machine learning pipeline have been automated so far. We highlight those subtasks that are still done manually—generally by a data scientist—and explain how this limits domain experts’ access to machine learning. Next, we introduce our novel level-based taxonomy for AutoML systems and define each level according to the scope of automation support provided. Finally, we lay out a roadmap for the future, pinpointing the research required to further automate the end-to-end machine learning pipeline and discussing important challenges that stand in the way of this ambitious goal.
Due to changes in our climate and constant loss of habitat for animals, new pathogens for humans are constantly erupting. SARS-CoV-2 virus, become so infectious and deadly that they put new challenge to the whole technological advancement of healthcare. Within this very decade, several other deadly virus outbreaks were witnessed by humans such as Zika virus, Ebola virus, MERS-coronavirus etc. and there might be even more infectious and deadlier diseases in the horizon. Though conventional techniques have succeeded in detecting these viruses to some extent, these techniques are time-consuming, costly, and require trained human-resources. Plasmonic metamaterial based biosensors might pave the way to low-cost rapid virus detection. So this review discusses in details, the latest development in plasmonics and metamaterial based biosensors for virus, viral particles and antigen detection and the future direction of research in this field.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.