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Staphylococcus spp. have been associated with cases of healthcare associated infections due to their high incidence in isolates from the hospital environment and their ability to cause infections in immunocompromised patients; synthesize biofilms on medical instruments, in the case of negative coagulase species; and change in genetic material, thus making it possible to disseminate genes that code for the acquisition of resistance mechanisms against the action of antibiotics. This study evaluated the presence of blaZ, femA, and mecA chromosomal and plasmid genes of Staphylococcus spp. using the qPCR technique. The results were associated with the phenotypic expression of resistance to oxacillin and penicillin G. We found that the chromosomal femA gene was present in a greater proportion in S. intermedius when compared with the other species analyzed, while the plasmid-borne mecA gene was prevalent in the S. aureus samples. The binary logistic regression performed to verify the association among the expression of the genes analyzed and the acquisition of resistance to oxacillin and penicillin G were not significant in any of the analyses, p > 0.05.

Background: Recurrent tonsillitis is one of the most common diseases in childhood, caused many times by ß-lactam-resistant S. aureus. The objective of this study was to investigate an alternative method to identify resistance to oxacillin/cefoxitin in S. aureus from hospitalized children with recurrent tonsillitis. Methods: The samples of S. aureus came from patients with recurrent tonsillitis and were used in 16S rRNA sequencing and an antibiogram test for identification and verifying resistance, after which HSI methodology were applied for separation of S. aureus resistances. Results: The S. aureus isolated showed sensitivity to oxacillin/cefoxitin and the diagnostic images show a visual description of the resistance different groups formed, that may be related to sensitivity and resistance to oxacillin/cefoxitin, characterizing the MRSA S. aureus. Conclusions: Samples that showed phenotypic resistance to oxacillin/cefoxitin were clearly separated from samples that did not show this resistance. A PLS-DA model predicted the presence of resistance to oxacillin/cefoxitin in S. aureus samples and it was possible to observe the pixels classified as MRSA. The HSI was able to successfully discriminate samples in replicas that were sensitive and resistant, based on the calibration model it received.

Monitoring and management of water levels has become an essential task in obtaining hydroelectric power. Activities such as water resources planning, supply basin management and flood forecasting are mediated and defined through its monitoring. Measurements, performed by sensors installed on the river facilities, are used for precisely information about water level estimations. Since weather conditions influence the results obtained by these sensors, it is necessary to have redundant approaches in order to maintain the high accuracy of the measured values. Staff gauge monitored by conventional cameras is a common redundancy method to keep track of the measurements. However, this method has low accuracy and is not reliable once it is monitored by human eyes. This work proposes to automate this process by using image processing methods of the staff gauge to measure and deep neural network to estimate the water level. To that end, three models of neural networks were compared: the residual networks (ResNet50), a MobileNetV2 and a proposed model of convolutional neural network (CNN). The results showed that ResNet50 and MobileNetV2 present inferior results compared to the proposed CNN.