Gas gangrene of different origin associated with Clostridium perfringens type A in three patients simultaneously hospitalized in a single department of orthopedics and traumatology in Poland

The objective of the study was to perform a comparative analysis of phenotypic and genetic similarity, determination of resistance profiles, detection of toxin-encoding genes and molecular typing of Clostridium perfringens isolates originating from patients with gas gangrene. The study encompassed three patients with a clinical and microbiological diagnosis of gas gangrene who were hospitalized in one of the hospitals of the Kujawsko-Pomorskie province in the same period of time between 8th April 2015 and 20th April 2015. The three C. perfrin­gens isolates studied had identical biochemical profiles. Two isolates had identical resistance patterns, while the third presented a different profile. Using the multiplex PCR method, all isolates showed the presence of cpa gene encoding α-toxin; furthermore, the presence of the cpb2 gene encoding β2-toxin was confirmed in two isolates. Genotyping with the use of pulsed field gel electrophoresis (PFGE) indicated that the isolates originating from the three studied patients represent three genetically different restrictive patterns which correspondedto three different clones – clone A, clone B and clone C. As a result of the study, it is possible to conclude that the studied patients simulta­neously hospitalized in a single Department of Orthopedics and Traumatology developed three different endogenous infections

The dynamics of vaginal and rectal Lactobacillus spp. flora in subsequent trimesters of pregnancy in healthy Polish women, assessed using the Sanger sequencing method

Background Lactobacilli play an important role in maintaining vaginal health and protection against bacterial infections in the genital tract. The aim of this study is to show the dynamics of changes of the vaginal and rectal Lactobacillus flora during pregnancy by using the Sanger sequencing method. Method The study included 31 healthy pregnant women without clinical signs of genitourinary infections. The material was taken in the three trimesters of pregnancy by vaginal and rectal swabs and grown on the MRS agar quantitatively to estimate the number of Lactobacillus spp. [CFU/ml]. Afterwards, 3 to 8 morphologically different lactobacilli colonies were taken for identification. Bacterial species identification was performed by 16 s rDNA sequence fragment analyses using the Sanger method. Results Among the patients tested, the most common species colonizing the vagina in the first trimester were: L. crispatus 29%, L. gasseri 19.4% and L. rhamnosus 16.1%, in the second trimester: L. crispatus 51.6%, L. gasseri 25.8%, L. rhamnosus 19.4% and L. amylovorus 16.1%, and in the third trimester the most common Lactobacillus species were: L. crispatus 25.8%, L. gasseri 25.8% and L. johnsonii 19.4%. In rectal species, the number decreased in the second and third trimesters in comparison to the first trimester (p = 0.003). An analysis of rectal dynamics showed that in the first trimester, the most common species were: L. johnsonii 19.4%, and L. plantarum 9.7%, in the second trimester: L. crispatus 9.7% and L. mucosae 6.5%, and in the third trimester: L. casei 9.7% and L. rhamnosus 9.7%. Individual dynamics of the Lactobacillus species composition showed variability, characterized by continuous, intermittent, or periodic colonization. The patients examined were mostly colonized by three Lactobacillus species in vagina (32.3%), whereas for the rectum, one Lactobacillus species during the whole pregnancy duration was common (32.3%). Conclusion This study showed that in the examined group of healthy, pregnant Polish women, the vaginal Lactobacillus flora, both qualitative and quantitative, was stable during the three subsequent trimesters. In contrast, the number of rectal Lactobacillus species dramatically decreased after the first trimester

The general status of patients and limited physical activity as risk factors of Methicillin-resistant Staphylococcus aureus occurrence in long-term care facilities residents in Krakow, Poland

BACKGROUND: The aim of this study was to investigate the epidemiology and resistance of methicillin-resistant Staphylococcus aureus (MRSA) isolates from long-term care facilities (LTCF) residents and to analyze the potential risk factors for MRSA occurrence, defined as MRSA colonization and/or infection. METHODS: Point prevalence (PPS) and prospective incidence continuous study (CS) was carried out on a group of 193 residents in 2009-2010. RESULTS: Overall MRSA occurred (with or without infection) among 17.6% of residents. There was 16 cases of infections with SA aetiology, of which 10 (58.8%) were caused by MRSA. The MRSA prevalence in PPS was 12.9%, in CS infection incidence rate was 5.2%. Factors associated with MRSA occurrence were: general status of patients, limited physical activity, wound infections (odds ratio, OR 4.6), ulcers in PPS (OR 2.1), diabetes (OR 1.6), urinary catheterization (OR 1.6) and stool incontinence (OR 1.2). CONCLUSIONS: Our data indicate a need for screening of MRSA before hospitalization or transfer to rehabilitation centres, especially in a group of residents with limitations in physical activity – i.e. with the highest risk of MRSA. Results also suggest the need for contact precautions in patients with high risk of MRSA occurrence, only. Focus on the high-risk population might be a solution for the cost-effective surveillance

Deep learning approach to bacterial colony classification

In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria. This method uses deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. To evaluate this approach and to make it comparable with other approaches, we provide a new dataset of images. DIBaS dataset (Digital Image of Bacterial Species) contains 660 images with 33 different genera and species of bacteria