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1.
Toxicol Mech Methods ; 33(4): 260-270, 2023 May.
Article in English | MEDLINE | ID: mdl-36093943

ABSTRACT

Renal failure caused by gentamicin is mainly mediated through oxidative damage, inflammation, and apoptosis. Hence, vitamin C and selenium, which have antioxidant, anti-inflammatory, and anti-apoptotic properties, and their nanoparticle forms, which have recently received attention, may reduce gentamicin-induced side effects. Therefore, the aim of this study was to investigate the therapeutic effects of vitamin C and selenium, and their nanoparticles on gentamicin-induced renal damage in male rats. 128 adult male Wistar rats were randomly divided into equal sixteen controlled and treated groups. Serum levels of uric acid, blood urea nitrogen, urea, and creatinine were measured. Renal levels of oxidative parameters such as MDA, SOD, and CAT and inflammatory parameters including IL-1ß, and TNF-α were measured. Renal expression of Nrf2, NF-κB, Bcl-2, caspase-3, BAX and mTORc1 was also evaluated. The results showed that gentamicin causes oxidative damage, inflammation, apoptosis and disruption of autophagy in kidney tissue in a dose-dependent manner. However, treatment with vitamin C, selenium and their nanoparticles could significantly improve these effects. Also, the results showed that the inflammatory and oxidative parameters and the expression of genes involved in them and apoptosis in the gentamicin groups treated with vitamin C nanoparticles and selenium nanoparticles reduced significantly compared to those treated with vitamin C and selenium. It can be concluded that vitamin C, selenium and their nanoparticles can improve gentamicin-induced kidney damage by inhibiting oxidative damage, inflammation and apoptosis-induced by autophagy, and can be a good option for kidney damage caused by gentamicin or as an adjunctive treatment to reduce its side effects.


Subject(s)
Ascorbic Acid , Gentamicins , Renal Insufficiency , Selenium , Animals , Male , Rats , Antioxidants/therapeutic use , Ascorbic Acid/pharmacology , Ascorbic Acid/therapeutic use , Inflammation , Kidney/drug effects , Oxidative Stress/drug effects , Rats, Wistar , Renal Insufficiency/chemically induced , Renal Insufficiency/drug therapy , Selenium/pharmacology , Selenium/therapeutic use
2.
Int Dent J ; 72(4): 436-447, 2022 Aug.
Article in English | MEDLINE | ID: mdl-35581039

ABSTRACT

AIM: The early detection of oral cancer (OC) at the earliest stage significantly increases survival rates. Recently, there has been an increasing interest in the use of artificial intelligence (AI) technologies in diagnostic medicine. This study aimed to critically analyse the available evidence concerning the utility of AI in the diagnosis of OC. Special consideration was given to the diagnostic accuracy of AI and its ability to identify the early stages of OC. MATERIALS AND METHODS: From the date of inception to December 2021, 4 databases (PubMed, Scopus, EBSCO, and OVID) were searched. Three independent authors selected studies on the basis of strict inclusion criteria. The risk of bias and applicability were assessed using the prediction model risk of bias assessment tool. Of the 606 initial records, 17 studies with a total of 7245 patients and 69,425 images were included. Ten statistical methods were used to assess AI performance in the included studies. Six studies used supervised machine learning, whilst 11 used deep learning. The results of deep learning ranged with an accuracy of 81% to 99.7%, sensitivity 79% to 98.75%, specificity 82% to 100%, and area under the curve (AUC) 79% to 99.5%. RESULTS: Results obtained from supervised machine learning demonstrated an accuracy ranging from 43.5% to 100%, sensitivity of 94% to 100%, specificity 16% to 100%, and AUC of 93%. CONCLUSIONS: There is no clear consensus regarding the best AI method for OC detection. AI is a valuable diagnostic tool that represents a large evolutionary leap in the detection of OC in its early stages. Based on the evidence, deep learning, such as a deep convolutional neural network, is more accurate in the early detection of OC compared to supervised machine learning.


Subject(s)
Artificial Intelligence , Mouth Neoplasms , Humans , Mouth Neoplasms/diagnosis , Neural Networks, Computer
3.
Braz. arch. biol. technol ; 64: e21210035, 2021. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1355818

ABSTRACT

Abstract Ginger is traditionally used as a sexual enhancer in folk medicine. Despite extensive studies on the effect of ginger on reproduction, the molecular mechanism of ginger prevention effect on ethanol-induced reproductive disorder is not fully understood. Twenty-four adult male ratswereallocated into control, ethanol (4 g/kg of body weight (BW)/day), ginger (250 mg/kg of BW/day) and ginger-ethanol group. Ginger and ethanol were administrated by gavage for 28 days. Testicular concentration of testosterone, TNF-α, and antioxidant enzymes activity and serum concentration of gonadotropins hormone and testosterone were measured. The gene expression of Nrf2 and NF-κB which regulate oxidative damage and inflammation, respectively, and StAR, P450scc and 17βHSD which are involved in testosterone synthesis were detected. Ethanol significantly decreased gonadotropin hormones, oxidative markers, expression of genes involved in testosterone synthesis and Nrf2, and in reverse significantly increased TNF-α, MDA and gene expression of NF-κB compared to control (p<0.05). While ginger could significantly improve all of the above factors compared to the ethanol group (p<0.05). These results were also supported by histological findings. It can be concluded that ginger prevents the ethanol-induced reproductive dysfunction by improving the gonadotropins, oxidative damage and inflammation and the genes involved in testosterone synthesis.

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