期刊论文详细信息
Brazilian Journal of Infectious Diseases | |
Influence of microbiome species in hard-to-heal wounds on disease severity and treatment duration | |
Smerkova, Kristyna1  Vesely, Radek2  Cihalova, Kristyna1  Adam, Vojtech1  Masarik, Michal1  Chudobova, Dagmar1  Guran, Roman1  Gumulec, Jaromir1  Dostalova, Simona1  Heger, Zbynek1  Kizek, Rene1  | |
[1]Mendel University in Brno, Zemedelska, Czech Republic | |
[2]Masaryk University and Trauma Hospital of Brno, Ponavka, Czech Republic | |
关键词: Bacterial strains; MALDI-TOF; Sequencing; Superficial wounds; | |
DOI : 10.1016/j.bjid.2015.08.013 | |
来源: Contexto | |
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【 摘 要 】
BACKGROUND: Infections, mostly those associated with colonization of wound by different pathogenic microorganisms, are one of the most serious health complications during a medical treatment. Therefore, this study is focused on the isolation, characterization, and identification of microorganisms prevalent in superficial wounds of patients (n = 50) presenting with bacterial infection.METHODS: After successful cultivation, bacteria were processed and analyzed. Initially the identification of the strains was performed through matrix-assisted laser desorption/ionization time-of-flight mass spectrometry based on comparison of protein profiles (2-30 kDa) with database. Subsequently, bacterial strains from infected wounds were identified by both matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and sequencing of 16S rRNA gene 108.RESULTS: The most prevalent species was Staphylococcus aureus (70%), and out of those 11% turned out to be methicillin-resistant (mecA positive). Identified strains were compared with patients' diagnoses using the method of artificial neuronal network to assess the association between severity of infection and wound microbiome species composition. Artificial neuronal network was subsequently used to predict patients' prognosis (n = 9) with 85% success.CONCLUSIONS: In all of 50 patients tested bacterial infections were identified. Based on the proposed artificial neuronal network we were able to predict the severity of the infection and length of the treatment.【 授权许可】
Unknown
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