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Braz. J. Pharm. Sci. (Online) ; 58: e20581, 2022. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1420374


Abstract Phytochemicals present in detox juices and probiotics have demonstrated protective effects on cardiovascular risk factors. The consumption of these products alone modulate metabolic mechanisms and biomarkers. However, the effects of the combination of detox juice and probiotics have not yet been evaluated on atherogenic parameters. A randomized controlled study was carried out with 40 healthy volunteers (20 men and 20 women), aged between 18 and 50 years old. The volunteers ingested 200mL of juice for 30 days. Before and after supplementation, the anthropometric and lipid profiles and plasma concentrations of TBARS, Myeloperoxidase, Glutathione, Protein and non-protein Thiols and Vitamin C were analyzed. A reduction in LDL-c (p=0.05), triglycerides (p=0.05) and a significant increase in HDL-c (p=0.002) was observed. There was a significant decrease in the concentrations of TBARS (p=0.01), myeloperoxidases (p=0.02) and a significant increase in the Vitamin C and GSH (p=0.01). There wasn`t improvement in anthropometric parameters and total cholesterol. The findings highlight that supplementation with probiotic detox juice improves the lipid and antioxidant profile, suggesting a possible positive effect in reducing the risk of cardiovascular disease in healthy volunteers. Nevertheless, more robust researches with a prolonged treatment period should be conducted.

Clin. biomed. res ; 40(3): 148-153, 2020. ilus, tab
Article in Portuguese | LILACS | ID: biblio-1248276


Introdução: Sistemas de inteligência artificial são tecnologias promissoras de assistência em saúde e diagnóstico laboratorial, que podem ser implementados como métodos de suporte para o diagnóstico de parasitoses intestinais. Este estudo objetivou desenvolver um software de IA que auxilia no diagnóstico laboratorial de parasitoses intestinais, com alta sensibilidade e especificidade. Métodos: O software foi desenvolvido utilizando duas redes neurais, Inception e MobileNet. Primeiro imagens de ovos dos parasitas Ascaris lumbricoides, Trichiuris trichiura, Taenia sp, Hymenolepis nana, Schistosoma mansoni e larvas de Strongyloides stercoralis, foram utilizados para treinar o banco de dados. Posteriormente 2.740 imagens cedidas pelo Laboratório de Parasitologia da Universidade do Oeste de Santa Catarina foram testadas no software. Resultados: O software apresentou sensibilidade de 82,3% (95% intervalo de confiança (IC), 71,9%-89,1%) e especificidade de 95,1% (95% IC, 94,3%-97,8%) para MobileNet e sensibilidade de 72,1% (95% IC, 52,6%-115%) e especificidade de 92,1% (95% IC, 91,7%-97,7%) para Inception. Conclusão: O software apresentou resultados promissores na análise de parasitas intestinais, reforçando que, no futuro, a presença de sistemas de suporte de diagnóstico das parasitoses pode vir a se tornar mais rápido e eficiente. (AU)

Introduction: Artificial intelligence systems are promising technologies for health care and laboratory diagnosis, which can be implemented as support methods for the diagnosis of intestinal parasitoses. This study aimed to develop an artificial intelligence software that assists the laboratory diagnosis of intestinal parasitoses with high sensitivity and specificity. Methods: The software was developed using two neural networks, Inception and MobileNet. First, images of eggs from the parasites Ascaris lumbricoides, Trichuris trichiura, Taenia sp., Hymenolepis nana, Schistosoma mansoni and Strongyloides stercoralis larvae were used to train the database. Then, 2,740 images provided by the Parasitology Laboratory of the Universidade do Oeste de Santa Catarina were tested in the software. Results: The software had a sensitivity of 82.3% (95% confidence interval [CI], 71.9% ­ 89.1%) and a specificity of 95.1% (95% CI, 94.3% ­ 97,8%) for MobileNet and a sensitivity of 72.1% (95% CI, 52.6% ­ 115%) and a specificity of 92.1% (95% CI, 91.7% ­ 97.7%) for Inception. Conclusion: The software showed promising results in the analysis of intestinal parasites, reinforcing that, in the future, the presence of diagnostic support systems for parasitoses may become faster and more efficient. (AU)

Humans , Artificial Intelligence , Helminthiasis/diagnosis , Intestinal Diseases, Parasitic/diagnosis , Parasitic Diseases/diagnosis , Software