RESUMO
BACKGROUND: Lactobacilli are common microorganisms in the human body. Some species were used as probiotics supplement for many purposes such as preventing necrotizing enterocolitis, or improving allergic diseases or diarrhea. Previously, Lactobacillus infection was thought of as contamination due to its low pathogenicity. However, there have been reports of invasive Lactobacillus infection in immunocompromised patients or patients with comorbidities. The purpose of this study was to analyze the clinical characteristics, antibiotic treatment and outcomes of pediatric patients with invasive Lactobacillus infection. METHODS: We retrospectively reviewed pediatric patients diagnosed with invasive Lactobacillus infection between 2004 and 2020. Invasive Lactobacillus infection was diagnosed if sterile sites yielded Lactobacillus spp. Clinical manifestations, chronic diseases, potential predisposing factors, medical treatments, antimicrobial susceptibility tests and outcomes were recorded. RESULTS: Fifteen pediatric patients were diagnosed with invasive Lactobacillus infection, accounting for 2.4% of total invasive Lactobacillus infections during the 16-year period. Eleven infections were bacteremia, two were intra-abdominal infections, and two were biliary tract infections. Fever was the most common symptom. Potential predisposing factors were immunocompromised status, central venous device, prolonged antibiotics use and receiving supplemented probiotics for at least one week. All patients survived with favorable outcomes. Most pathogens were identified as Lactobacillus spp, and two were Lactobacillus rhamnosus, which were related to supplemented probiotics. The antimicrobial susceptibility tests showed that Lactobacilli were all sensitive to ampicillin but resistant to glycopeptides. CONCLUSION: Invasive Lactobacillus infections in pediatric patients were rare. Despite its low pathogenicity, Lactobacillus could cause invasive infection in those immunocompromised patients.
RESUMO
Metallography is the study of the structure of metals and alloys. Metallographic analysis can be regarded as a detection tool to assist in identifying a metal or alloy, to evaluate whether an alloy is processed correctly, to inspect multiple phases within a material, to locate and characterize imperfections such as voids or impurities, or to find the damaged areas of metallographic images. However, the defect detection of metallography is evaluated by human experts, and its automatic identification is still a challenge in almost every real solution. Deep learning has been applied to different problems in computer vision since the proposal of AlexNet in 2012. In this study, we propose a novel convolutional neural network architecture for metallographic analysis based on a modified residual neural network (ResNet). Multi-scale ResNet (M-ResNet), the modified method, improves efficiency by utilizing multi-scale operations for the accurate detection of objects of various sizes, especially small objects. The experimental results show that the proposed method yields an accuracy of 85.7% (mAP) in recognition performance, which is higher than existing methods. As a consequence, we propose a novel system for automatic defect detection as an application for metallographic analysis.
