22,694 research outputs found
Antimicrobial and antibiofilm activity and machine learning classification analysis of essential oils from different mediterranean plants against pseudomonas aeruginosa
Pseudomonas aeruginosa
is a ubiquitous organism and opportunistic pathogen that can cause persistent infections due to its peculiar antibiotic resistance mechanisms and to its ability to adhere and form biofilm. The interest in the development of new approaches for the prevention and treatment of biofilm formation has recently increased. The aim of this study was to seek new non-biocidal agents able to inhibit biofilm formation, in order to counteract virulence rather than bacterial growth and avoid the selection of escape mutants. Herein, different essential oils extracted from Mediterranean plants were analyzed for their activity againstP. aeruginosa. Results show that they were able to destabilize biofilm at very low concentration without impairing bacterial viability. Since the action is not related to a bacteriostatic/bactericidal activity onP. aeruginosa, the biofilm change of growth in presence of the essential oils was possibly due to a modulation of the phenotype. To this aim, application of machine learning algorithms led to the development of quantitative activity-composition relationships classification models that allowed to direct point out those essential oil chemical components more involved in the inhibition of biofilm production. The action of selected essential oils on sessile phenotype make them particularly interesting for possible applications such as prevention of bacterial contamination in the community and in healthcare environments in order to prevent human infections. We assayed 89 samples of different essential oils asP. aeruginosaanti-biofilm. Many samples inhibitedP. aeruginosabiofilm at concentrations as low as 48.8 µg/mL. Classification of the models was developed through machine learning algorithms
Partially-Latent Class Models (pLCM) for Case-Control Studies of Childhood Pneumonia Etiology
In population studies on the etiology of disease, one goal is the estimation
of the fraction of cases attributable to each of several causes. For example,
pneumonia is a clinical diagnosis of lung infection that may be caused by
viral, bacterial, fungal, or other pathogens. The study of pneumonia etiology
is challenging because directly sampling from the lung to identify the
etiologic pathogen is not standard clinical practice in most settings. Instead,
measurements from multiple peripheral specimens are made. This paper introduces
the statistical methodology designed for estimating the population etiology
distribution and the individual etiology probabilities in the Pneumonia
Etiology Research for Child Health (PERCH) study of 9; 500 children for 7 sites
around the world. We formulate the scientific problem in statistical terms as
estimating the mixing weights and latent class indicators under a
partially-latent class model (pLCM) that combines heterogeneous measurements
with different error rates obtained from a case-control study. We introduce the
pLCM as an extension of the latent class model. We also introduce graphical
displays of the population data and inferred latent-class frequencies. The
methods are tested with simulated data, and then applied to PERCH data. The
paper closes with a brief description of extensions of the pLCM to the
regression setting and to the case where conditional independence among the
measures is relaxed.Comment: 25 pages, 4 figures, 1 supplementary materia
Genetic factors regulating lung vasculature and immune cell functions associate with resistance to pneumococcal infection
Streptococcus pneumoniae is an important human pathogen responsible for high mortality and morbidity worldwide. The susceptibility to pneumococcal infections is controlled by as yet unknown genetic factors. To elucidate these factors could help to develop new medical treatments and tools to identify those most at risk. In recent years genome wide association studies (GWAS) in mice and humans have proved successful in identification of causal genes involved in many complex diseases for example diabetes, systemic lupus or cholesterol metabolism. In this study a GWAS approach was used to map genetic loci associated with susceptibility to pneumococcal infection in 26 inbred mouse strains. As a result four candidate QTLs were identified on chromosomes 7, 13, 18 and 19. Interestingly, the QTL on chromosome 7 was located within S. pneumoniae resistance QTL (Spir1) identified previously in a linkage study of BALB/cOlaHsd and CBA/CaOlaHsd F2 intercrosses. We showed that only a limited number of genes encoded within the QTLs carried phenotype-associated polymorphisms (22 genes out of several hundred located within the QTLs). These candidate genes are known to regulate TGFb signalling, smooth muscle and immune cells functions. Interestingly, our pulmonary histopathology and gene expression data demonstrated, lung vasculature plays an important role in resistance to pneumococcal infection. Therefore we concluded that the cumulative effect of these candidate genes on vasculature and immune cells functions as contributory factors in the observed differences in susceptibility to pneumococcal infection. We also propose that TGFbmediated regulation of fibroblast differentiation plays an important role in development of invasive pneumococcal disease.This work was supported by the European Union-funded Pneumopath Project HEALTH-F3-2009-222983. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer-reviewedPublisher Versio
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Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach
Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence
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