دورية أكاديمية
أعرض في EDS

Influence of microbiome species in hard-to-heal wounds on disease severity and treatment duration

التفاصيل البيبلوغرافية
العنوان: Influence of microbiome species in hard-to-heal wounds on disease severity and treatment duration
المؤلفون: Dagmar Chudobova, Kristyna Cihalova, Roman Guran, Simona Dostalova, Kristyna Smerkova, Radek Vesely, Jaromir Gumulec, Michal Masarik, Zbynek Heger, Vojtech Adam, Rene Kizek
المصدر: Brazilian Journal of Infectious Diseases, Vol 19, Iss 6, Pp 604-613 (2015)
بيانات النشر: Elsevier, 2015.
سنة النشر: 2015
المجموعة: LCC:Infectious and parasitic diseases
LCC:Microbiology
مصطلحات موضوعية: Infectious and parasitic diseases, RC109-216, Microbiology, QR1-502
الوصف: 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. Keywords: Bacterial strains, MALDI-TOF, Sequencing, Superficial wounds
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1413-8670
Relation: http://www.sciencedirect.com/science/article/pii/S1413867015001889; https://doaj.org/toc/1413-8670
DOI: 10.1016/j.bjid.2015.08.013
URL الوصول: https://doaj.org/article/aeb54bb813d641ce943559e1f983178e
رقم الأكسشن: edsdoj.b54bb813d641ce943559e1f983178e
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:14138670
DOI:10.1016/j.bjid.2015.08.013