2020
DOI: 10.1038/s41598-020-59553-8
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Essential oils against bacterial isolates from cystic fibrosis patients by means of antimicrobial and unsupervised machine learning approaches

Abstract: Recurrent and chronic respiratory tract infections in cystic fibrosis (CF) patients result in progressive lung damage and represent the primary cause of morbidity and mortality. Staphylococcus aureus (S. aureus) is one of the earliest bacteria in CF infants and children. Starting from early adolescence, patients become chronically infected with Gram-negative non-fermenting bacteria, and Pseudomonas aeruginosa (P. aeruginosa) is the most relevant and recurring. Intensive use of antimicrobial drugs to fight lung… Show more

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Cited by 35 publications
(47 citation statements)
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“…This study is focused on the search of new strategies to tackle the critical topic of antibiotic resistance in chronic wound infections. Staphylococcus aureus and P. aeruginosa are microbial species characterized by a significant multidrug-resistance and innovative plans for treatments are required 20 , 21 . These microorganisms represent the most frequent combined species isolated in polymicrobial wound infections and are capable to express synergism through an increased antibiotic tolerance level compared with the single species culture 5 .…”
Section: Discussionmentioning
confidence: 99%
“…This study is focused on the search of new strategies to tackle the critical topic of antibiotic resistance in chronic wound infections. Staphylococcus aureus and P. aeruginosa are microbial species characterized by a significant multidrug-resistance and innovative plans for treatments are required 20 , 21 . These microorganisms represent the most frequent combined species isolated in polymicrobial wound infections and are capable to express synergism through an increased antibiotic tolerance level compared with the single species culture 5 .…”
Section: Discussionmentioning
confidence: 99%
“…It is thus likely that the mutations which occurred in SaROEO during the evolution assay would not be related to general mechanisms of bacterial resistance, but rather to a specific resistance to OEO. In this regard, the clinical use of EO to combat bacterial infections and prevent AMR needs to be reconsidered [ 6 , 38 ].…”
Section: Discussionmentioning
confidence: 99%
“…This second simulation quickly diverged from the first because the system changed from the one that had been used to train the CNN. For a third simulation, the adaptive feedback algorithm (12) was also applied with y as in (13), which adaptively tuned both the PCA components and the current of solenoid S1 in order to track the time-varying output distribution of the first simulation, as shown in Fig. 5 (B-F).…”
Section: Demonstration At Hires Uedmentioning
confidence: 99%
“…To give a few concrete examples, various ML methods have now been demonstrated for a wide range of systems such as molecular and materials science studies 3 , for use in optical communications and photonics 4 , to accurately predict battery life 5 , to accelerate lattice Monte Carlo simulations using neural networks 6 , for studying complex networks 7 , for characterizing surface microstructure of complex materials 8 , for chemical discovery 9 , for active matter analysis by using deep neural networks to track objects 10 , for particle physics 11 , for antimicrobial studies 12 , for pattern recognition for optical microscopy images of metallurgical microstructures 13 , for learning Perovskit bandgaps 14 , for real-time mapping of electron backscatter diffraction (EBSD) patterns to crystal orientations 15 , for speeding up simulation-based accelerator optimization studies 16 , for Bayesian optimization of free electron lasers (FEL) 17 , for temporal power reconstruction of FELs 18 , for various applications at the Large Hadron Collider (LHC) at CERN including optics corrections and detecting faulty beam position monitors [19][20][21] , for reconstruction of a storage ring's linear optics based on Bayesian inference 22 , to analyze beam position monitor placement in accelerators to find arrangements with the lowest probable predictive errors based on Bayesian Gaussian regression 23 , for temporal shaping of electron bunches in particle accelerators 24 , for stabilization of source properties in synchrotron light sources 25 , and to represent many-body interactions with restricted-Boltzmann-machine neural networks 26 .…”
Section: Introductionmentioning
confidence: 99%