World Health Organization considered diabetes as one of the 21st century epidemics, estimating that over 10% of the world population is diabetic or at high risk. Self-assessment studies indicate that diabetic patients consider chronic wounds to affect their quality of life more dramatically than vision loss or renal failure. In addition to being the main reason for diabetic patients hospitalization, the economic burden of diabetic chronic wounds is close to 1% of UK and USA health systems budgets, which exceeds the funds allocated to the treatment of some types of cancer in both countries. Among the factors preceding the emergence of chronic diabetic wounds, also designated diabetic foot ulcers (DFU), hygiene and pressure in specific areas are under patient control, while others are still far from being understood. A triple impairment in the innervation, immune responses and vascularization associated to DFU have been extensively studied by the scientific community. However, the skin natural microbiota has only recently emerged as having a tremendous impact on DFU emergence and evolution to chronicity. Despite the great inter- and intra-variability of microbial colonizers, ongoing efforts are now focused on deciphering the impact of commensal and pathogenic microbiota on DFU etiology, as well as the mechanisms of interkingdom microbial-host communication. This review summarizes recent work in this context and offers new microbiological perspectives that may hold potential in the prevention and treatment of chronic diabetic wounds.

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Celiac disease or gluten-dependent enteropathy is a chronic autoimmune pathology triggered by dietary gluten in genetic predisposed individuals, mediated by transglutaminase 2 IgA autoantibodies and associated with a deteriorating immune and inflammatory response. This leads to intestinal villous atrophy, impairing the intestinal mucosa structure and function of secretion, digestion, and absorption. The result is macro- and micronutrient deficiency, including fat soluble vitamins and minerals, and a consequent nutritional status depletion. A lifelong gluten-free diet is the only available treatment for celiac patients in order to assure normal intestinal mucosa and remission of gastrointestinal symptoms. However, a gluten-free diet can itself cause other nutritional deficiencies due to its restrictive nature regarding gluten-containing cereals. A group of gluten-free cereals, known as pseudocereals, is increasingly recognized as valuable options for gluten-free diets due to their high nutritional value. Amaranth, quinoa, millet, and buckwheat are examples of gluten-free nutrient-dense grains that can be used as alternatives to the conventional gluten-containing grains and improve the variety and nutritional quality of the celiac diet. Current work reviews the nutritional pitfalls of a gluten-free diet and analyses how pseudocereals can contribute to revert those deficiencies and optimize the nutritional value of this mandatory diet for the celiac population.

Background : While Enterobacteriaceae bacteria are commonly found in the healthy human gut, their colonization of other body parts can potentially evolve into serious infections and health threats. We investigate a graph-based machine learning model to predict risks of inpatient colonization by multidrug-resistant (MDR) Enterobacteriaceae. Methods: Colonization prediction was defined as a binary task, where the goal is to predict whether a patient is colonized by MDR Enterobacteriaceae in an undesirable body part during their hospital stay. To capture topological features, interactions among patients and healthcare workers were modeled using a graph structure, where patients are described by nodes and their interactions are described by edges. Then, a graph neural network (GNN) model was trained to learn colonization patterns from the patient network enriched with clinical and spatiotemporal features. Results: The GNN model achieves performance between 0.91 and 0.96 area under the receiver operating characteristic curve (AUROC) when trained in inductive and transductive settings, respectively, up to 8% above a logistic regression baseline (0.88). Comparing network topologies, the configuration considering ward-related edges (0.91 inductive, 0.96 transductive) outperforms the configurations considering caregiver-related edges (0.88, 0.89) and both types of edges (0.90, 0.94). For the top 3 most prevalent MDR Enterobacteriaceae, the AUROC varies from 0.94 for Citrobacter freundii up to 0.98 for Enterobacter cloacae using the best-performing GNN model. Conclusion: Topological features via graph modeling improve the performance of machine learning models for Enterobacteriaceae colonization prediction. GNNs could be used to support infection prevention and control programs to detect patients at risk of colonization by MDR Enterobacteriaceae and other bacteria families.

Nontuberculous mycobacteria (NTM) are ubiquitous in nature and recognized agents of opportunistic infection, which is often aggravated by their intrinsic resistance to antimicrobials, poorly defined therapeutic strategies and by the lack of new drugs. However, evaluation of their prevalence in anthropogenic environments and the associated antimicrobial resistance profiles have been neglected. In this work, we sought to determine minimal inhibitory concentrations of 25 antimicrobials against 5 NTM isolates recovered from a tertiary-care hospital surfaces. Antimicrobial susceptibilities of 5 other Corynebacterineae isolated from the same hospital were also determined for their potential clinical relevance. Our phylogenetic study with each of the NTM isolates confirm they belong to Mycobacterium obuense, Mycobacterium mucogenicum and Mycobacterium paragordonae species, the latter initially misidentified as strains of M. gordonae, a species frequently isolated from patients with NTM disease in Portugal. In contrast to other strains, the M. obuense and M. mucogenicum examined here were resistant to several of the CLSI-recommended drugs, suggestive of multidrug-resistant profiles. Surprisingly, M. obuense was susceptible to vancomycin. Their genomes were sequenced allowing detection of gene erm (erythromycin resistance methylase) in M. obuense, explaining its resistance to clarithromycin. Remarkably, and unlike other strains of the genus, the Corynebacterium isolates were highly resistant to penicillin, ciprofloxacin and linezolid. This study highlights the importance of implementing effective measures to screen, accurately identify and control viable NTM and closely related bacteria in hospital settings. Our report on the occurrence of rare NTM species with antibiotic susceptibility profiles that are distinct from those of the corresponding Type strains, along with unexpected resistance mechanisms detected seem to suggest that resistance may be more common than previously thought and also a potential threat to frail and otherwise vulnerable inpatients.