996 resultados para evolutionary medicine


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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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In modern society, combatting cardiovascular and metabolic diseases has been highlighted as an urgent global challenge. In recent decades, the scientific literature has identified that behavioral variables (e.g. smoking, unhealthy diet and physical inactivity) are related to the development of these outcomes and, therefore, preventive actions should focus on the promotion of physical exercise practice and a healthy diet, as well as combatting the smoking habit from an early age. The promotion of physical exercise in the general population has been suggested as a relevant goal by significant health organizations around the world. On the other hand, recent literature has indicated that physical exercise performed in early life prevents the development of diabetes mellitus, dyslipidemia and arterial hypertension during adulthood, although this protective effect seems to be independent of the physical activity performed during adulthood. Apparently, the interaction between physical exercise and human growth in early life constitutes an issue which is not completely understood by sports medicine. The aim of the present review was therefore to discuss recent evidence on the effects of physical exercise performed during childhood and adolescence on cardiovascular and metabolic outcomes in adulthood.

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Y chromosome markers have been widely studied due to their various applications in the fields of forensic and evolutionary genetics. In this study, 35 Y-SNPs and 17 Y-STRs were genotyped in 253 males from the State of Espirito Santo, Brazil. A total of 18 haplogroups and 243 haplotypes were detected; the haplogroup and haplotype diversities were 0.7794 and 0.9997, respectively. Genetic distance analysis using the Y-STR data showed no statistically significant differences between Espirito Santo and other admixed populations from Brazil. The classification of paternal lineages based on haplogroups showed a predominant European contribution (85.88 %), followed by African (11.37 %) and Amerindian (2.75 %) contributions.

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Peritonitis continues to be a major complication of peritoneal dialysis (PD), and adequate treatment is crucial for a favorable outcome. There is no consensus regarding the optimal therapeutic regimen, and few prospective controlled studies have been published. The objective of this manuscript is to review the results of PD peritonitis treatment reported in narrative reviews, systematic reviews, and proportional meta-analyses. Two narrative reviews, the only existing systematic review and its update published between 1991 and 2014 were included. In addition, we reported the results of a proportional meta-analysis published by our group. Results from systematic reviews of randomized control trials (RCT) and quasi-RCT were not able to identify any optimal antimicrobial treatment, but glycopeptide regimens were more likely to achieve a complete cure than a first generation cephalosporin. Compared to urokinase, simultaneous catheter removal and replacement resulted in better outcomes. Continuous and intermittent IP antibiotic use had similar outcomes. Intraperitoneal antibiotics were superior to intravenous antibiotics in reducing treatment failure. In the proportional meta-analysis of RCTs and the case series, the resolution rate (86%) of ceftazidime plus glycopeptide as initial treatment was significantly higher than first generation cephalosporin plus aminoglycosides (66%) and glycopeptides plus aminoglycosides (75%). Other comparisons of regimens used for either initial treatment or treatment of gram-positive rods or gram-negative rods did not show statistically significant differences. The superiority of a combination of a glycopeptide and a third generation cephalosporin was also reported by a narrative review study published in 1991, which reported an 88% resolution rate.

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In this paper we deal with the problem of boosting the Optimum-Path Forest (OPF) clustering approach using evolutionary-based optimization techniques. As the OPF classifier performs an exhaustive search to find out the size of sample's neighborhood that allows it to reach the minimum graph cut as a quality measure, we compared several optimization techniques that can obtain close graph cut values to the ones obtained by brute force. Experiments in two public datasets in the context of unsupervised network intrusion detection have showed the evolutionary optimization techniques can find suitable values for the neighborhood faster than the exhaustive search. Additionally, we have showed that it is not necessary to employ many agents for such task, since the neighborhood size is defined by discrete values, with constrain the set of possible solution to a few ones.