Blood is considered to be a sterile microenvironment, in which bacteria appear only periodically. Previously used methods allowed only for the detection of either viable bacteria with low sensitivity or selected species of bacteria. The Next-Generation Sequencing method (NGS) enables the identification of all bacteria in the sample with their taxonomic classification. We used NGS for the analysis of blood samples from healthy volunteers (n = 23) and patients with sepsis (n = 62) to check whether any bacterial DNA exists in the blood of healthy people and to identify bacterial taxonomic profile in the blood of septic patients. The presence of bacterial DNA was found both in septic and healthy subjects; however, bacterial diversity was significantly different (P = 0.002) between the studied groups. Among healthy volunteers, a significant predominance of anaerobic bacteria (76.2 %), of which most were bacteria of the order Bifidobacteriales (73.0 %), was observed. In sepsis, the majority of detected taxa belonged to aerobic or microaerophilic microorganisms (75.1 %). The most striking difference was seen in the case of Actinobacteria phyla, the abundance of which was decreased in sepsis (P < 0.001) and Proteobacteria phyla which was decreased in the healthy volunteers (P < 0.001). Our research shows that bacterial DNA can be detected in the blood of healthy people and that its taxonomic composition is different from the one seen in septic patients. Detection of bacterial DNA in the blood of healthy people may suggest that bacteria continuously translocate into the blood, but not always cause sepsis; this observation can be called DNAemia.Electronic supplementary materialThe online version of this article (doi:10.1007/s10096-016-2805-7) contains supplementary material, which is available to authorized users.

The aetiology of inflammatory bowel diseases (IBD) seems to be strongly connected to changes in the enteral microbiome. The dysbiosis pattern seen in Crohn’s disease (CD) differs among published studies depending on patients’ age, disease phenotype and microbiome research methods. The aims was to investigate microbiome in treatment-naive paediatric patients to get an insight into its structure at the early stage of the disease in comparison to healthy. Stool samples were obtained from controls and newly diagnosed patients prior to any intervention. Microbiota was analysed by 16SrRNAnext-generation-sequencing (NGS). Differences in the within-sample phylotype richness and evenness (alpha diversity) were detected between controls and patients. Statistically significant dissimilarities between samples were present for all used metrics. We also found a significant increase in the abundance of OTUs of the Enterococcus genus and reduction in, among others, Bifidobacterium (B. adolescentis), Roseburia (R.faecis), Faecalibacterium (F. prausnitzii), Gemmiger (G. formicilis), Ruminococcus (R. bromii) and Veillonellaceae (Dialister). Moreover, differences in alpha and beta diversities in respect to calprotectin and PCDAI were observed: patients with calprotectin <100 µg/g and with PCDAI below 10 points vs those with calprotectin >100 µg/g and mild (10–27.7 points), moderate (27.5–40 points) or severe (>40 points) CD disease activity had higher richness and diversity of gut microbiota. The results of our study highlight reduced diversity and dysbiosis at the earliest stage of the disease. Microbial imbalance and low abundance of butyrate-producing bacteria, including Bifidobacterium adolescentis, may suggest benefits of microbial modification therapy.

Introduction Scientific data indicate a possible influence of gut microbiota on the development of type 1 and type 2 diabetes mellitus (T1DM and T2DM, respectively). Sequence analysis of 16S ribosomal RNA identified several hundred bacterial species of the intestinal ecosystem, most of which cannot be cultured. Objectives We aimed to evaluate gut microbiota composition in adult patients with T1DM and T2DM and establish a link between microbiological test results and patients' clinical data. Patients and methods We examined DNA isolated from fecal samples in 3 groups: healthy volunteers (n = 23), patients with T1DM (n = 22), and patients with T2DM (n = 23). Next‑generation sequencing was performed on the MiSeq platform. Results At the phylum level, the Firmicutes bacteria prevailed (>77%) in all groups. At the taxonomic levels L2 (phylum) and L6 (genus), significant differences were demonstrated in bacterial profiles, particularly in the T2DM group. A negative correlation was observed between several genera of bacteria and the percentage of glycated hemoglobin A1c in the T2DM group, while a positive correlation was revealed between bacteria belonging to the genus Bifidobacterium and high‑density lipoprotein cholesterol levels in both T1DM and T2DM groups. Conclusions Our results provide grounds for conducting research in the field of gut microbiota in order to develop individualized therapy for patients with diabetes based on modifying the microbiota composition, as a new method for controlling glycemia. Next‑generation sequencing allows a rapid identification of the DNA of all bacteria present in the sample and their taxonomic classification.

Numerous scientific studies confirm that, apart from environmental and genetic factors, a significant role is played by gastrointestinal microbiota in the aetiology of type 2 diabetes and obesity. Currently, scientists mainly focus on the distal intestinal microbiota, while the equally important proximal parts of the intestine are overlooked. The aim of the study was a qualitative analysis of the structure of the duodenal mucosa microbiota in groups of patients with obesity and with type 2 diabetes and where obesity qualified for bariatric surgery: sleeve gastrectomy. The microbiological results obtained were compared with some clinical parameters. As a result, it was possible to determine the microbiological core that the treatment and control groups had in common, including phyla: Firmicutes, Proteobacteria, and Actinobacteria. The patients with obesity and with type 2 diabetes and obesity presented a significantly lower number of genus Bifidobacterium compared to healthy subjects. Furthermore, the numbers of Bifidobacterium were positively correlated with the high density lipoprotein (HDL) concentration in the groups under study. The obtained results indicate that bacteria of the genus Bifidobacterium should be considered in the future in the context of a potential biomarker in the progress of type 2 diabetes and obesity.

Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identification of the species due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identification process up to 10 days. Such a delay in the implementation of targeted therapy may be grave in consequence as the mortality rate for immunosuppressed patients is high. In this paper, we apply a machine learning approach based on deep neural networks and bag-of-words to classify microscopic images of various fungi species. Our approach makes the last stage of biochemical identification redundant, shortening the identification process by 2-3 days, and reducing the cost of the diagnosis.