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1.
Diagnostics (Basel) ; 13(8)2023 Apr 21.
Article in English | MEDLINE | ID: mdl-37189600

ABSTRACT

BACKGROUND: Artificial Intelligence (AI) is an area of computer science/engineering that is aiming to spread technological systems. The COVID-19 pandemic caused economic and public health turbulence around the world. Among the many possibilities for using AI in the medical field is FreeStyle Libre® (FSL), which uses a disposable sensor inserted into the user's arm, and a touchscreen device/reader is used to scan and retrieve other continuous monitoring of glucose (CMG) readings. The aim of this systematic review is to summarize the effectiveness of FSL blood glucose monitoring during the COVID-19 pandemic. METHODS: This systematic review was carried out in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) and was registered in the international prospective register of systematic reviews (PROSPERO: CRD42022340562). The inclusion criteria considered studies involving the use of the FSL device during the COVID-19 pandemic and published in English. No publication date restrictions were set. The exclusion criteria were abstracts, systematic reviews, studies with patients with other diseases, monitoring with other equipment, patients with COVID-19, and bariatrics patients. Seven databases were searched (PubMed, Scopus, Embase, Web of Science, Scielo, PEDro and Cochrane Library). The ACROBAT-NRSI tool (A Cochrane Risk of Bias Assessment Tool for Non-Randomized Studies) was used to evaluate the risk of bias in the selected articles. RESULTS: A total of 113 articles were found. Sixty-four were excluded because they were duplicates, 39 were excluded after reading the titles and abstracts, and twenty articles were considered for full reading. Of the 10 articles analyzed, four articles were excluded because they did not meet the inclusion criteria. Thus, six articles were included in the current systematic review. It was observed that among the selected articles, only two were classified as having serious risk of bias. It was shown that FSL had a positive impact on glycemic control and on reducing the number of individuals with hypoglycemia. CONCLUSION: The findings suggest that the implementation of FSL during COVID-19 confinement in this population can be confidently stated to have been effective in diabetes mellitus patients.

2.
Biosci Rep ; 37(4)2017 Aug 31.
Article in English | MEDLINE | ID: mdl-28784934

ABSTRACT

The consequences of treatment involving the use of a natural product and whole body vibration (WBV) exercise have been investigated. The aim of the present study was to evaluate the effects of the joint treatment with an aqueous extract of Chenopodium ambrosioides and WBV on physiological parameters in rats. Wistar rats (n=20) were divided equally into four groups: control group (CG), treated with C. ambrosioides (CHE) group, exposed to 50 Hz of mechanial vibration (VBR), and treated with C. ambrosioides and exposed to 50 Hz of mechanical vibration (VBR + CHE) daily for 6 weeks. The body mass of the animals was determined weekly, the feed intake and the stool consistency were measured daily. One day after the 6 weeks of treatment, samples of blood were collected and used for biochemical analysis. Along 6 weeks, there was an increase (P<0.001) in the feed intake in VBR group and a decrease in the CHE group in comparison with other groups. The levels of the enzyme aspartate aminotransferase (AST) in VBR + CHE group decreased (P<0.05) in comparison with other groups. No differences were found in body mass and stool consistency. WBV altered the feed intake without directly affecting the body mass. Moreover, WBV in association with C. ambrosioides caused alteration in the enzymatic activity of AST.


Subject(s)
Chenopodiaceae/chemistry , Eating/drug effects , Exercise Test , Plant Extracts/pharmacology , Vibration/therapeutic use , Animals , Aspartate Aminotransferases/metabolism , Biomarkers/blood , Feces , Male , Physical Conditioning, Animal , Rats , Rats, Wistar , Statistics, Nonparametric , Time Factors
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