Detection of human Mastadenovirus C in wild guinea pigs (Cavia aperea aperea) feces.

The Adenoviridae family is composed by a high diversity of viruses that are extremely resistant in environment and are frequently excreted in animal reservoir feces for long periods. The knowledge of adenovirus (AdV) diversity among wild species may be important for the understanding of the epidemiology of putative emerging diseases. Cavia aperea aperea, commonly known as wild guinea pigs, wild cavies, or preas, are small herbivorous rodents widely distributed throughout South America and classified in Caviidae family, as well as domestic guinea pigs and capybaras. In order to investigate their potential role as reservoir of zoonotic agents, the present study aimed to verify the presence of AdV in fecal samples of 14 preas from Northeast Brazil. When submitted to nested PCR, two out of 14 samples (14.28%) were positive for AdV and classified as human Mastadenovirus C (HAdV-C) using DNA sequencing and phylogenetic analysis. Wild guinea pigs are synanthropic rodents that live in close contact with humans. The investigation of viral agents in rodents is important due to their potential role as reservoirs of human and animal pathogens. Moreover, the present work presents the first known evidence of HAdV in wild guinea pig stool samples, which may represent both the impact of anthropogenic pollution to wild animals and an important knowledge in terms of human health.

Classification of antimicrobial resistance using artificial neural networks and the relationship of 38 genes associated with the virulence of escherichia coli isolates from broilers

Avian pathogenic Escherichia coli (APEC) is responsible for various pathological processes in birds and is considered as one of the principal causes of morbidity and mortality, associated with economic losses to the poultry industry. The objective of this study was to demonstrate that it is possible to predict antimicrobial resistance of 256 samples (APEC) using 38 different genes responsible for virulence factors, through a computer program of artificial neural networks (ANNs). A second target was to find the relationship between (PI) pathogenicity index and resistance to 14 antibiotics by statistical analysis. The results showed that the RNAs were able to make the correct classification of the behavior of APEC samples with a range from 74.22 to 98.44%, and make it possible to predict antimicrobial resistance. The statistical analysis to assess the relationship between the pathogenic index (PI) and resistance against 14 antibiotics showed that these variables are independent, i.e. peaks in PI can happen without changing the antimicrobial resistance, or the opposite, changing the antimicrobial resistance without a change in PI.

Escherichia coli patogênica (APEC) para as aves é responsável por vários processos patológicos em aves, sendo considerado como uma das principais causas de morbidade e mortalidade, associado com perdas econômicas para a indústria avícola. O objetivo do presente trabalho foi demonstrar que é possível predizer a resistência antimicrobiana de 256 amostras de APEC utilizando 38 genes responsáveis por distintos fatores de virulência, através de um programa computacional de redes neurais artificiais (RNAs). O segundo objetivo foi verificar por análise estatística a relação entre o índice de patogenicidade (IP) e a resistência aos 14 antimicrobianos. Os resultados demostraram que as RNAs foram capazes de realizar a classificação correta do comportamento das amostras APEC com uma amplitude de 74,22 a 98,44%, desta forma tornando possível predizer a resistência antimicrobiana. A análise estatística realizada para verificar a relação entre o IP e a resistência aos antimicrobianos demostrou que estas variáveis são independentes, ou seja, podem haver picos no IP sem alteração na resistência, ou até mesmo o contrário, alteração na resistência antimicrobiana sem mudança no IP.