Background Medicinal plants are used to treat various disorders, including diabetes, globally in a range of formulations. While attention has mainly been on the aerial plant parts, there are only a few review studies to date that are focused on the natural constituents present in the plant roots with health benefits. Thus, the present study was performed to review in vivo studies investigating the antidiabetic potential of the natural compounds in plant roots. Methods We sorted relevant data in 2001–2019 from scientific databases and search engines, including Web of Knowledge, PubMed, ScienceDirect, Medline, Reaxys, and Google Scholar. The class of phytochemicals, plant families, major compounds, active constituents, effective dosages, type of extracts, time of experiments, and type of diabetic induction were described. Results In our literature review, we found 104 plants with determined antidiabetic activity in their root extracts. The biosynthesis pathways and mechanism of actions of the most frequent class of compounds were also proposed. The results of this review indicated that flavonoids, phenolic compounds, alkaloids, and phytosteroids are the most abundant natural compounds in plant roots with antidiabetic activity. Phytochemicals in plant roots possess different mechanisms of action to control diabetes, including inhibition of α-amylase and α-glucosidase enzymes, oxidative stress reduction, secretion of insulin, improvement of diabetic retinopathy/nephropathy, slow the starch digestion, and contribution against hyperglycemia. Conclusion This review concludes that plant roots are a promising source of bioactive compounds which can be explored to develop against diabetes and diabetes-related complications. Graphical abstract
Objectives. To avoid worsening from mild, moderate, and severe diseases and to reduce mortality, it is necessary to identify the subpopulation that is more vulnerable to the development of COVID-19 unfavorable consequences. This study aims to investigate the demographic information, prevalence rates of common comorbidities among negative and positive real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) patients, and the association between SARS-CoV-2 cycle threshold (Ct) at hospital admission, demographic data, and outcomes of the patients in a large population in Northern Iran. Methods. This large retrospective cross-sectional study was performed from 7 March to 20 December 2020. Demographic data, including gender, age, underlying diseases, clinical outcomes, and Ct values, were obtained from 8,318 cases suspected of COVID-19, who were admitted to four teaching hospitals affiliated to Babol University of Medical Sciences (MUBABOL), in the north of Iran. Results. Since 7 March 2020, the data were collected from 8,318 cases suspected of COVID-19 (48.5% female and 51.5% male) with a mean age of 53 ± 25.3 years. Among 8,318 suspected COVID-19 patients, 3,250 (39.1%) had a positive rRT-PCR result; 1,632 (50.2%) patients were male and 335 (10.3%) patients died during their hospital stay. The distribution of positive rRT-PCR revealed that most patients (464 (75.7%)) had a Ct between 21 and 30 (Group B). Conclusion. Elderly patients, lower Ct, patients having at least one comorbidity, and male cases were significantly associated with increased risk for COVID-19-related mortality. Moreover, mortality was significantly higher in patients with diabetes, kidney disease, and respiratory disease.
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.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.