Screening of Lactobacillus spp. for the prevention of Pseudomonas aeruginosa pulmonary infections.

BACKGROUND: Pseudomonas aeruginosa is an opportunistic pathogen that significantly increases morbidity and mortality in nosocomial infections and cystic fibrosis patients. Its pathogenicity especially relies on the production of virulence factors or resistances to many antibiotics. Since multiplication of antibiotic resistance can lead to therapeutic impasses, it becomes necessary to develop new tools for fighting P. aeruginosa infections. The use of probiotics is one of the ways currently being explored. Probiotics are microorganisms that exert a positive effect on the host's health and some of them are known to possess antibacterial activities. Since most of their effects have been shown in the digestive tract, experimental data compatible with the respiratory environment are strongly needed. The main goal of this study was then to test the capacity of lactobacilli to inhibit major virulence factors (elastolytic activity and biofilm formation) associated with P. aeruginosa pathogenicity. RESULTS: Sixty-seven lactobacilli were isolated from the oral cavities of healthy volunteers. These isolates together with 20 lactobacilli isolated from raw milks, were tested for their capacity to decrease biofilm formation and activity of the elastase produced by P. aeruginosa PAO1. Ten isolates, particularly efficient, were accurately identified using a polyphasic approach (API 50 CHL, mass-spectrometry and 16S/rpoA/pheS genes sequencing) and typed by pulsed-field gel electrophoresis (PFGE). The 8 remaining strains belonging to the L. fermentum (6), L. zeae (1) and L. paracasei (1) species were sensitive to all antibiotics tested with the exception of the intrinsic resistance to vancomycin. The strains were all able to grow in artificial saliva. CONCLUSION: Eight strains belonging to L. fermentum, L. zeae and L. paracasei species harbouring anti-elastase and anti-biofilm properties are potential probiotics for fighting P. aeruginosa pulmonary infections. However, further studies are needed in order to test their innocuity and their capacity to behave such as an oropharyngeal barrier against Pseudomonas aeruginosa colonisation in vivo.

Evaluating the Bias in Hospital Data: Automatic Preprocessing of Patient Pathways Algorithm Development and Validation Study.

BACKGROUND: The optimization of patient care pathways is crucial for hospital managers in the context of a scarcity of medical resources. Assuming unlimited capacities, the pathway of a patient would only be governed by pure medical logic to meet at best the patient's needs. However, logistical limitations (eg, resources such as inpatient beds) are often associated with delayed treatments and may ultimately affect patient pathways. This is especially true for unscheduled patients-when a patient in the emergency department needs to be admitted to another medical unit without disturbing the flow of planned hospitalizations. OBJECTIVE: In this study, we proposed a new framework to automatically detect activities in patient pathways that may be unrelated to patients' needs but rather induced by logistical limitations. METHODS: The scientific contribution lies in a method that transforms a database of historical pathways with bias into 2 databases: a labeled pathway database where each activity is labeled as relevant (related to a patient's needs) or irrelevant (induced by logistical limitations) and a corrected pathway database where each activity corresponds to the activity that would occur assuming unlimited resources. The labeling algorithm was assessed through medical expertise. In total, 2 case studies quantified the impact of our method of preprocessing health care data using process mining and discrete event simulation. RESULTS: Focusing on unscheduled patient pathways, we collected data covering 12 months of activity at the Groupe Hospitalier Bretagne Sud in France. Our algorithm had 87% accuracy and demonstrated its usefulness for preprocessing traces and obtaining a clean database. The 2 case studies showed the importance of our preprocessing step before any analysis. The process graphs of the processed data had, on average, 40% (SD 10%) fewer variants than the raw data. The simulation revealed that 30% of the medical units had >1 bed difference in capacity between the processed and raw data. CONCLUSIONS: Patient pathway data reflect the actual activity of hospitals that is governed by medical requirements and logistical limitations. Before using these data, these limitations should be identified and corrected. We anticipate that our approach can be generalized to obtain unbiased analyses of patient pathways for other hospitals.

Prevalence and dynamics of Lactobacillus sp. in the lower respiratory tract of patients with cystic fibrosis.

No prevalence or dynamics analysis of Lactobacilli in the lung of cystic fibrosis (CF) patients has yet been conducted. In order to use them as probiotics in the treatment of Pseudomonas aeruginosa infection, we describe their lung epidemiology. Over a period of 8 months, we analyzed 279 sputum samples from 124 CF patients classified according to their P. aeruginosa Leeds status of colonization. A total of 137 strains belonging to 11 species were isolated. The prevalence of carriage was 61%. No difference in species diversity or frequency was observed according to Leeds criteria. The next step will be to focus on the strain level.