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

Essential oils against bacterial isolates from cystic fibrosis patients by means of antimicrobial and unsupervised machine learning approaches

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 infections inevitably leads to the onset of antibiotic resistant bacterial strains. New antimicrobial compounds should be identified to overcome antibiotic resistance in these patients. Recently interesting data were reported in literature on the use of natural derived compounds that inhibited in vitro S. aureus and P. aeruginosa bacterial growth. Essential oils, among these, seemed to be the most promising. In this work is reported an extensive study on 61 essential oils (EOs) against a panel of 40 clinical strains isolated from CF patients. To reduce the in vitro procedure and render the investigation as convergent as possible, machine learning clusterization algorithms were firstly applied to pick-up a fewer number of representative strains among the panel of 40. This approach allowed us to easily identify three EOs able to strongly inhibit bacterial growth of all bacterial strains. Interestingly, the EOs antibacterial activity is completely unrelated to the antibiotic resistance profile of each strain. Taking into account the results obtained, a clinical use of EOs could be suggested

Essential oils biofilm modulation activity, chemical and machine learning analysis. Application on staphylococcus aureus isolates from cystic fibrosis patients

Bacterial biofilm plays a pivotal role in chronic Staphylococcus aureus (S. aureus) infection and its inhibition may represent an important strategy to develop novel therapeutic agents. The scientific community is continuously searching for natural and “green alternatives” to chemotherapeutic drugs, including essential oils (EOs), assuming the latter not able to select resistant strains, likely due to their multicomponent nature and, hence, multitarget action. Here it is reported the biofilm production modulation exerted by 61 EOs, also investigated for their antibacterial activity on S. aureus strains, including reference and cystic fibrosis patients’ isolated strains. The EOs biofilm modulation was assessed by Christensen method on five S. aureus strains. Chemical composition, investigated by GC/MS analysis, of the tested EOs allowed a correlation between biofilm modulation potency and putative active components by means of machine learning algorithms application. Some EOs inhibited biofilm growth at 1.00% concentration, although lower concentrations revealed dierent biological profile. Experimental data led to select antibiofilm EOs based on their ability to inhibit S. aureus biofilm growth, which were characterized for their ability to alter the biofilm organization by means of SEM studies

Towards evidence-based conservation of subterranean ecosystems

Subterranean ecosystems are among the most widespread environments on Earth, yet we still have poor knowledge of their biodiversity. To raise awareness of subterranean ecosystems, the essential services they provide, and their unique conservation challenges, 2021 and 2022 were designated International Years of Caves and Karst. As these ecosystems have traditionally been overlooked in global conservation agendas and multilateral agreements, a quantitative assessment of solution-based approaches to safeguard subterranean biota and associated habitats is timely. This assessment allows researchers and practitioners to understand the progress made and research needs in subterranean ecology and management. We conducted a systematic review of peer-reviewed and grey literature focused on subterranean ecosystems globally (terrestrial, freshwater, and saltwater systems), to quantify the available evidence-base for the effectiveness of conservation interventions. We selected 708 publications from the years 1964 to 2021 that discussed, recommended, or implemented 1,954 conservation interventions in subterranean ecosystems. We noted a steep increase in the number of studies from the 2000s while, surprisingly, the proportion of studies quantifying the impact of conservation interventions has steadily and significantly decreased in recent years. The effectiveness of 31% of conservation interventions has been tested statistically. We further highlight that 64% of the reported research occurred in the Palearctic and Nearctic biogeographic regions. Assessments of the effectiveness of conservation interventions were heavily biased towards indirect measures (monitoring and risk assessment), a limited sample of organisms (mostly arthropods and bats), and more accessible systems (terrestrial caves). Our results indicate that most conservation science in the field of subterranean biology does not apply a rigorous quantitative approach, resulting in sparse evidence for the effectiveness of interventions. This raises the important question of how to make conservation efforts more feasible to implement, cost-effective, and long-lasting. Although there is no single remedy, we propose a suite of potential solutions to focus our efforts better towards increasing statistical testing and stress the importance of standardising study reporting to facilitate meta-analytical exercises. We also provide a database summarising the available literature, which will help to build quantitative knowledge about interventions likely to yield the greatest impacts depending upon the subterranean species and habitats of interest. We view this as a starting point to shift away from the widespread tendency of recommending conservation interventions based on anecdotal and expert-based information rather than scientific evidence, without quantitatively testing their effectiveness.Peer reviewe