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Molecules ; 26(15)2021 Jul 31.
Article in English | MEDLINE | ID: covidwho-1346517

ABSTRACT

Thin-layer chromatography (TLC) bioautography is an evolving technology that integrates the separation and analysis technology of TLC with biological activity detection technology, which has shown a steep rise in popularity over the past few decades. It connects TLC with convenient, economic and intuitive features and bioautography with high levels of sensitivity and specificity. In this study, we discuss the research progress of TLC bioautography and then establish a definite timeline to introduce it. This review summarizes known TLC bioautography types and practical applications for determining antibacterial, antifungal, antitumor and antioxidant compounds and for inhibiting glucosidase, pancreatic lipase, tyrosinase and cholinesterase activity constitutes. Nowadays, especially during the COVID-19 pandemic, it is important to identify original, natural products with anti-COVID potential compounds from Chinese traditional medicine and natural medicinal plants. We also give an account of detection techniques, including in situ and ex situ techniques; even in situ ion sources represent a major reform. Considering the current technical innovations, we propose that the technology will make more progress in TLC plates with higher separation and detection technology with a more portable and extensive scope of application. We believe this technology will be diffusely applied in medicine, biology, agriculture, animal husbandry, garden forestry, environmental management and other fields in the future.


Subject(s)
Chromatography, Thin Layer/methods , Drug Discovery/methods , Luminescent Measurements/methods , Animals , Anti-Infective Agents/isolation & purification , Antineoplastic Agents/isolation & purification , Antioxidants/isolation & purification , Enzyme Inhibitors/isolation & purification , Humans , Microbial Sensitivity Tests/methods , Plant Extracts/chemistry , Plant Extracts/pharmacology , Plants, Medicinal/chemistry , Sensitivity and Specificity
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