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

Bioactive Phyto-Compounds with Antimicrobial Effects and AI: Results of a Desk Research Study

Resistance of microorganisms to antibiotics represents a formidable global challenge, manifesting in intricate public health ramifications including escalated mortality rates and augmented healthcare costs. The current efforts to manage antimicrobial resistance (AMR) are limited mainly to the standard therapeutic approaches. The aim of this study is to present and analyze the role of artificial intelligence (AI) in the search for new phyto-compounds and novel interactions with antimicrobial effects. The ambition of the current research study is to support researchers by providing summarized information and ideas for future research in the battle with AMR. Inevitably, the AI role in healthcare is growing exponentially. The reviewed AI models reveal new data on essential oils (EOs) as potential therapeutic agents. In terms of antibacterial activity, EOs show activity against MDR bacteria, reduce resistance by sensitizing bacteria to the action of antibiotics, and improve therapeutic efficiency when combined with antibiotics. AI models can also serve for the detailed study of other therapeutic applications of EOs such as respiratory diseases, immune diseases, neurodegenerative diseases, and oncological diseases. The last 5 years have seen an increasing application of AI in the search for potential plant sources to control AMR. For the time being, the application of machine-learning (ML) models is greater in the studies of EOs. Future attention of research teams may also be directed toward a more efficient search for plant antimicrobial peptides (PAMPs). Of course, investments in this direction are a necessary preface, but the excitement of new possibilities should not override the role of human intelligence in directing research processes. In this report, tradition meets innovation to address the "silent pandemic" of AMR.

Screening and selection of essential oils for an intranasal spray against bovine respiratory pathogens based on antimicrobial, antiviral, immunomodulatory, and antibiofilm activities

Introduction

The rise in antibiotic resistant pathogens associated with bovine respiratory disease (BRD) poses a serious challenge, particularly to the beef feedlot industry, as they currently depend on antibiotics to prevent BRD to mitigate the financial burden (approx. $1 billion annual loss) inflicted by BRD-associated high mortality and morbidity in feedlot cattle. Thus, there is an impetus need for the development of antimicrobial alternative strategies against BRD. This study aimed to screen and select candidate essential oils (EOs) for the development of an intranasal EO spray that can inhibit BRD pathogens and promote microbiota-mediated respiratory health.

Methods

The effects of selected EOs (ajowan, cinnamon leaf, citronella, grapefruit, fennel, and thyme) on a bovine nasopharyngeal microbiota culture were evaluated using 16S rRNA gene sequencing. The microbiota culture was enriched by incubating nasopharyngeal swabs obtained from finishing beef heifers in brain heart infusion broth with and without EOs (0.025%, v/v). These EOs were then also evaluated for their immunomodulatory effects on bovine turbinate (BT) cells by analyzing the concentrations of 15 cytokines and chemokines in cell culture after 24 h incubation. The crystal violet assay was done to assess the antibiofilm activity of EOs against Escherichia coli UMN026 strain. Finally, 15 EOs were screened for their antiviral activity against the bovine viral diarrhea virus 1 (BVDV-1) using BT cells and a fluorescence-based method.

Results

Ajowan, fennel, and thyme resulted in a moderate reduction of overall nasopharyngeal microbiota growth with significant alterations of both alpha and beta diversity, and the relative abundance of predominant bacterial families (e.g., increasing Enterobacteriaceae and decreasing Moraxellaceae) compared to the control (p < 0.05). Co-incubation of BT cells with selected EOs resulted in minimal alterations in cytokine and chemokine levels (p > 0.05). Ajowan, thyme, fennel, and cinnamon leaf exhibited antibiofilm activity at concentrations of 0.025 and 0.05%. Reduction of BVDV-1 replication in BT cells was observed with thyme (strong), and ajowan and citronella (moderate) at 0.0125% concentration.

Discussion

Accordingly, ajowan, thyme, fennel, cinnamon leaf, and citronella EOs were selected for further development as an intranasal EO spray to prevent and control of BRD pathogens in feedlot cattle.

A single intranasal dose of essential oil spray confers modulation of the nasopharyngeal microbiota and short-term inhibition of Mannheimia in feedlot cattle: a pilot study

Five essential oils (EOs) were previously characterized in vitro and identified as candidate EOs for the development of an intranasal EO spray to mitigate bovine respiratory disease (BRD) pathogens. In the present study, these EOs were evaluated for their potential to (i) reduce BRD pathogens, (ii) modulate nasopharyngeal microbiota, and (iii) influence animal performance, feeding behavior and immune response when a single dose administered intranasally to feedlot cattle. Forty beef steer calves (7-8 months old, Initial body weight = 284 ± 5 kg [SE]) received either an intranasal EO spray (ajowan, thyme, fennel, cinnamon leaf, and citronella) or PBS (Control; n = 20/group) on day 0. Deep nasopharyngeal swabs were collected on days (d) -1, 1, 2, 7, 14, 28, and 42 and processed for 16S rRNA gene sequencing, qPCR, and culturing. Significant effects of EO on community structure (d1), microbial richness and diversity, relative abundance of some dominant phyla (d1, d2, and d14), and the overall interaction network structure of the nasopharyngeal microbiota were detected. The relative abundance of Mannheimia was lower in the EO calves (4.34%) than in Control calves (10.4%) on d2, and M. haemolytica prevalence on d7 as compared to control calves. Feed intake, average daily gain, feeding behavior, and blood cell counts were not affected by EO treatment. Overall, a single intranasal dose of EO spray resulted in moderate modulation of nasopharyngeal microbiota and short-term inhibition of Mannheimia while not influencing animal performance, feeding behavior or immune response. Our study, for the first time, shows the potential use of intranasal EO to mitigate BRD in feedlot cattle.

Screening of the activity of sixty essential oils against plasmodium early mosquito stages in vitro and machine learning analysis reveals new putative inhibitors of malaria parasites

Malaria, an infectious disease with a tremendous impact on human health is caused by Plasmodium parasites, and transmitted by Anopheles mosquitoes. New approaches to control the disease involve transmission blocking strategies aiming to target the parasite in the mosquito. Here, we investigated the putative inhibitory activity of essential oils and their components on the early mosquito stages of the parasite. We employed an in vitro assay of gametocyte-to-ookinete development of the rodent model parasite Plasmodium berghei combined with high content screening. 60 essential oils with known composition were tested. The results revealed that fifteen EOs had inhibitory activity. Furthermore, a machine learning approach was used to identify the putative inhibitory components. Five of the most important chemical components indicated by the machine learning-based models were actually confirmed by the experimental approach. This combined approach was used for the first time to identify the potential transmission blocking activity of essential oils and single components at the zygote and ookinete stages.

In Vitro Antioxidant and In Vivo Antigenotoxic Features of a Series of 61 Essential Oils and Quantitative Composition-Activity Relationships Modeled through Machine Learning Algorithms

The antioxidant activity of essential oils (EOs) is an important and frequently studied property, yet it is not sufficiently understood in terms of the contribution of EOs mixtures' constituents and biological properties. In this study, a series of 61 commercial EOs were first evaluated as antioxidants in vitro, following as closely as possible the cellular pathways of reactive oxygen species (ROS) generation. Hence, EOs were assessed for the ability either to chelate metal ions, thus interfering with ROS generation within the respiratory chain, or to neutralize 2,2-diphenyl-1-picrylhydrazyl (DPPH•) and lipid peroxide radicals (LOO•), thereby halting lipid peroxidation, as well as to neutralize 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid cation radicals (ABTS•+) and hydroxyl radicals (OH•), thereby preventing the ROS species from damaging DNA nucleotides. Showing noteworthy potencies to neutralize all of the radicals at the ng/mL level, the active EOs were also characterized as protectors of DNA double strands from damage induced by peroxyl radicals (ROO•), emerging from 2,2'-azobis-2-methyl-propanimidamide (AAPH) as a source, and OH•, indicating some genome protectivity and antigenotoxicity effectiveness in vitro. The chemical compositions of the EOs associated with the obtained activities were then analyzed by means of machine learning (ML) classification algorithms to generate quantitative composition-activity relationships (QCARs) models (models published in the AI4EssOil database available online). The QCARs models enabled us to highlight the key features (EOSs' chemical compounds) for exerting the redox potencies and to define the partial dependencies of the features, viz. percentages in the mixture required to exert a given potency. The ML-based models explained either the positive or negative contribution of the most important chemical components: limonene, linalool, carvacrol, eucalyptol, α-pinene, thymol, caryophyllene, p-cymene, eugenol, and chrysanthone. Finally, the most potent EOs in vitro, Ylang-ylang (Cananga odorata (Lam.)) and Ceylon cinnamon peel (Cinnamomum verum J. Presl), were promptly administered in vivo to evaluate the rescue ability against redox damage caused by CCl4, thereby verifying their antioxidant and antigenotoxic properties either in the liver or in the kidney.

Effect of Different Soil Treatments on Production and Chemical Composition of Essential Oils Extracted from Foeniculum vulgare Mill., Origanum vulgare L. and Thymus vulgaris L

The aim of the study was to investigate how essential oil production and associated chemical composition and related biological activity could be influenced by different cultivation treatments and distillation methods. Foeniculum vulgare Mill. (fennel), Origanum vulgare L. (oregano) and Thymus vulgaris L. (thyme) were cultivated in absence of any fertilizer (control) and in presence of three different fertilizers: a chemical one with augmented mineral phosphorus and potassium, a second added with hydrolyzed organic substance and mineral phosphorus and potassium (organic-mineral) and a third one treated with a high content of organic nitrogen of protein origin (organic). The plants were subjected to steam distillation using two modalities, recycled and continuous, to obtain 32 essential oil samples. Chemical composition analysis was performed using gas chromatography-mass spectrometry; in vitro antimicrobial activity was evaluated using a broth microdilution method. In general, the recycled distillation method appeared to have a slightly higher yield than the continuous method. The "mineral" and "organic-mineral" treatments resulted in a higher yield compared to the "organic" or "control" treatments, and this was particularly evident in the recycled method. The "control" plants had a lower yield of essential oils. Anethole (13.9-59.5%) and estragole (13.4-52.2%) were the main constituents of the fennel oils; p-cymene and its derivatives carvacrol and thymol were the main constituents of the oregano and thyme samples. The antimicrobial activity of the thyme oils on Staphylococcus aureus ranged from 0.31 to 0.16% (v/v); a lower effect of the oregano samples and no activity of the fennel samples were observed. The essential oils failed to inhibit the growth of Pseudomonas aeruginosa strains.

Photodynamic Therapy, Probiotics, Acetic Acid, and Essential Oil in the Treatment of Chronic Wounds Infected with Pseudomonas aeruginosa

As a prevalent medical problem that burdens millions of patients across the world, chronic wounds pose a challenge to the healthcare system. These wounds, often existing as a comorbidity, are vulnerable to infections. Consequently, infections hinder the healing process and complicate clinical management and treatment. While antibiotic drugs remain a popular treatment for infected chronic wounds, the recent rise of antibiotic-resistant strains has hastened the need for alternative treatments. Future impacts of chronic wounds are likely to increase with aging populations and growing obesity rates. With the need for more effective novel treatments, promising research into various wound therapies has seen an increased demand. This review summarizes photodynamic therapy, probiotics, acetic acid, and essential oil studies as developing antibiotic-free treatments for chronic wounds infected with Pseudomonas aeruginosa. Clinicians may find this review informative by gaining a better understanding of the state of current research into various antibiotic-free treatments. Furthermore. this review provides clinical significance, as clinicians may seek to implement photodynamic therapy, probiotics, acetic acid, or essential oils into their own practice.

Serratiopeptidase Affects the Physiology of Pseudomonas aeruginosa Isolates from Cystic Fibrosis Patients

Pseudomonas aeruginosa is frequently involved in cystic fibrosis (CF) airway infections. Biofilm, motility, production of toxins and the invasion of host cells are different factors that increase P. aeruginosa's virulence. The sessile phenotype offers protection to bacterial cells and resistance to antimicrobials and host immune attacks. Motility also contributes to bacterial colonization of surfaces and, consequently, to biofilm formation. Furthermore, the ability to adhere is the prelude for the internalization into lung cells, a common immune evasion mechanism used by most intracellular bacteria, such as P. aeruginosa. In previous studies we evaluated the activity of metalloprotease serratiopeptidase (SPEP) in impairing virulence-related properties in Gram-positive bacteria. This work aimed to investigate SPEP's effects on different physiological aspects related to the virulence of P. aeruginosa isolated from CF patients, such as biofilm production, pyoverdine and pyocyanin production and invasion in alveolar epithelial cells. Obtained results showed that SPEP was able to impair the attachment to inert surfaces as well as adhesion/invasion of eukaryotic cells. Conversely, SPEP's effect on pyocyanin and pyoverdine production was strongly strain-dependent, with an increase and/or a decrease of their production. Moreover, SPEP seemed to increase swarming motility and staphylolytic protease production. Our results suggest that a large number of clinical strains should be studied in-depth before drawing definitive conclusions. Why different strains sometimes react in opposing ways to a specific treatment is of great interest and will be the object of future studies. Therefore, SPEP affects P. aeruginosa's physiology by differently acting on several bacterial factors related to its virulence.

Antimicrobial Efficiency of Some Essential Oils in Antibiotic-Resistant Pseudomonas aeruginosa Isolates

Pseudomonas aeruginosa is a non-fermentative Gram-negative opportunistic pathogen, frequently encountered in difficult-to-treat hospital-acquired infections and also wastewaters. The natural resistance of this pathogen, together with the frequent occurrence of multidrug-resistant strains, make current antibiotic therapy inefficient in treating P. aeruginosa infections. Antibiotic therapy creates a huge pressure to select resistant strains in clinical settings but also in the environment, since high amounts of antibiotics are released in waters and soil. Essential oils (EOs) and plant-derived compounds are efficient, ecologic, and sustainable alternatives in the management of various diseases, including infections. In this study, we evaluated the antibacterial effects of four commercial essential oils, namely, tea tree, thyme, sage, and eucalyptus, on 36 P. aeruginosa strains isolated from hospital infections and wastewaters. Bacterial strains were characterized in terms of virulence and antimicrobial resistance. The results show that most strains expressed soluble pore toxin virulence factors such as lecithinase (89–100%) and lipase (72–86%). All P. aeruginosa strains were positive for alginate encoding gene and 94.44% for protease IV; most of the strains were exotoxin producers (i.e., 80.56% for the ExoS gene, 77.78% for the ExoT gene, while the ExoU gene was present in 38.98% of the strains). Phospholipase-encoding genes (plc) were identified in 91.67/86.11% of the cases (plcH/plcN genes). A high antibiotic resistance level was identified, most of the strains being resistant to cabapenems and cephalosporins. Cabapenem resistance was higher in hospital and hospital wastewater strains (55.56–100%) as compared to those in urban wastewater. The most frequently encountered encoding genes were for extended spectrum β-lactamases (ESBLs), namely, bla_CTX-M (83.33% of the strains), bla_SHV (80.56%), bla_GES (52.78%), and bla_VEB (13.89%), followed by carbapenemase-encoding genes (bla_VIM, 8.33%). Statistical comparison of the EOs’ antimicrobial results showed that thyme gave the lowest minimum inhibitory concentrations (MIC) and minimum biofilm eradication concentrations (MBEC) in P. aeruginosa-resistant isolates, making this EO a competitive candidate for the development of efficient and ecologic antimicrobial alternatives.

Essential Oils Biofilm Modulation Activity and Machine Learning Analysis on Pseudomonas aeruginosa Isolates from Cystic Fibrosis Patients

The opportunistic pathogen Pseudomonas aeruginosa is often involved in airway infections of cystic fibrosis (CF) patients. It persists in the hostile CF lung environment, inducing chronic infections due to the production of several virulence factors. In this regard, the ability to form a biofilm plays a pivotal role in CF airway colonization by P. aeruginosa. Bacterial virulence mitigation and bacterial cell adhesion hampering and/or biofilm reduced formation could represent a major target for the development of new therapeutic treatments for infection control. Essential oils (EOs) are being considered as a potential alternative in clinical settings for the prevention, treatment, and control of infections sustained by microbial biofilms. EOs are complex mixtures of different classes of organic compounds, usually used for the treatment of upper respiratory tract infections in traditional medicine. Recently, a wide series of EOs were investigated for their ability to modulate biofilm production by different pathogens comprising S. aureus, S. epidermidis, and P. aeruginosa strains. Machine learning (ML) algorithms were applied to develop classification models in order to suggest a possible antibiofilm action for each chemical component of the studied EOs. In the present study, we assessed the biofilm growth modulation exerted by 61 commercial EOs on a selected number of P. aeruginosa strains isolated from CF patients. Furthermore, ML has been used to shed light on the EO chemical components likely responsible for the positive or negative modulation of bacterial biofilm formation.

A Shaving Proteomic Approach to Unveil Surface Proteins Modulation of Multi-Drug Resistant Pseudomonas aeruginosa Strains Isolated From Cystic Fibrosis Patients

Cystic fibrosis (CF) is the most common rare disease caused by a mutation of the CF transmembrane conductance regulator gene encoding a channel protein of the apical membrane of epithelial cells leading to alteration of Na⁺ and K⁺ transport, hence inducing accumulation of dense and sticky mucus and promoting recurrent airway infections. The most detected bacterium in CF patients is Pseudomonas aeruginosa (PA) which causes chronic colonization, requiring stringent antibiotic therapies that, in turn induces multi-drug resistance. Despite eradication attempts at the first infection, the bacterium is able to utilize several adaptation mechanisms to survive in hostile environments such as the CF lung. Its adaptive machinery includes modulation of surface molecules such as efflux pumps, flagellum, pili and other virulence factors. In the present study we compared surface protein expression of PA multi- and pan-drug resistant strains to wild-type antibiotic-sensitive strains, isolated from the airways of CF patients with chronic colonization and recent infection, respectively. After shaving with trypsin, microbial peptides were analyzed by tandem-mass spectrometry on a high-resolution platform that allowed the identification of 174 differentially modulated proteins localized in the region from extracellular space to cytoplasmic membrane. Biofilm assay was performed to characterize all 26 PA strains in term of biofilm production. Among the differentially expressed proteins, 17 were associated to the virulome (e.g., Tse2, Tse5, Tsi1, PilF, FliY, B-type flagellin, FliM, PyoS5), six to the resistome (e.g., OprJ, LptD) and five to the biofilm reservoir (e.g., AlgF, PlsD). The biofilm assay characterized chronic antibiotic-resistant isolates as weaker biofilm producers than wild-type strains. Our results suggest the loss of PA early virulence factors (e.g., pili and flagella) and later expression of virulence traits (e.g., secretion systems proteins) as an indicator of PA adaptation and persistence in the CF lung environment. To our knowledge, this is the first study that, applying a shaving proteomic approach, describes adaptation processes of a large collection of PA clinical strains isolated from CF patients in early and chronic infection phases.

Anti-Virulence Properties of Coridothymus capitatus Essential Oil against Pseudomonas aeruginosa Clinical Isolates from Cystic Fibrosis Patients

Pseudomonas aeruginosa is an opportunistic pathogen responsible for nosocomial infections, and is often involved in airway infections of cystic fibrosis (CF) patients. P. aeruginosa virulence is related to its ability to form biofilm, trigger different types of motilities, and produce toxins (for example, bacterial pigments). In this scenario, essential oils (EOs) have gained notoriety for their role in phenotype modulation, including virulence modulation. Among different EOs previously analyzed, herein we investigated the activity of Coridothymus capitatus EO (CCEO) against specific virulence factors produced by P. aeruginosa isolated from CF patients. CCEO showed inhibition of new biofilm formation and reduction in mature biofilm in about half of the tested strains. On selected strains, SEM analysis provided interesting information regarding CCEO action in a pre-adhesion assay. CCEO treatment showed a dramatic modification of the extracellular matrix (ECM) structure. Our results clearly showed a drastic reduction in pyocyanin production (between 84% and 100%) for all tested strains in the presence of CCEO. Finally, CCEO was also able to strongly affect P. aeruginosa swarming and swimming motility for almost all tested strains. In consideration of the novel results obtained on clinical strains isolated from CF patients, CCEO may be a potential candidate to limit P. aeruginosa virulence.

Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition-Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils

Scientific investigation on essential oils composition and the related biological profile are continuously growing. Nevertheless, only a few studies have been performed on the relationships between chemical composition and biological data. Herein, the investigation of 61 assayed essential oils is reported focusing on their inhibition activity against Microsporum spp. including development of machine learning models with the aim of highlining the possible chemical components mainly related to the inhibitory potency. The application of machine learning and deep learning techniques for predictive and descriptive purposes have been applied successfully to many fields. Quantitative composition-activity relationships machine learning-based models were developed for the 61 essential oils tested as Microsporum spp. growth modulators. The models were built with in-house python scripts implementing data augmentation with the purpose of having a smoother flow between essential oils' chemical compositions and biological data. High statistical coefficient values (Accuracy, Matthews correlation coefficient and F1 score) were obtained and model inspection permitted to detect possible specific roles related to some components of essential oils' constituents. Robust machine learning models are far more useful tools to reveal data augmentation in comparison with raw data derived models. To the best of the authors knowledge this is the first report using data augmentation to highlight the role of complex mixture components, in particular a first application of these data will be for the development of ingredients in the dermo-cosmetic field investigating microbial species considering the urge for the use of natural preserving and acting antimicrobial agents.

An adaptive approach to machine learning for compact particle accelerators

Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

Abd El-Hack M. Using essential oils to overcome bacterial biofilm formation and their antimicrobial resistance

The increase of resistant bacteria puts a huge pressure on the antimicrobials in current use. Antimicrobial resistance (AMR) results from antibiotic misuse and abuse over many years and is a global financial burden. New polices must be developed for the use of antimicrobials and to continue research efforts to mitigate AMR. It is essential to target the most harmful bacteria and concentrate on their mechanisms of resistance to develop successful antimicrobials. Essential oils (EOs) are occur naturally in plants and have long been used as antimicrobials, but most have not been researched. This review explores EOs as alternative antimicrobials, investigating their ability to decrease or inhibit biofilm formation, and assess their ability to contribute to AMR control. Low concentrations of EOs can inhibit Gram-positive and Gram-negative pathogenic bacteria. Some EOs have demonstrated strong anti-biofilm activities. If EOs are successful against biofilm formation, particularly in bacteria developing AMR, they could be incorporated into new antimicrobials. Therefore, there is a need to investigate these EOs' potential, particularly for surface disinfection, and against bacteria from food, clinical and non-clinical environments.

Anti-Virulence Activity of the Cell-Free Supernatant of the Antarctic Bacterium Psychrobacter sp. TAE2020 against Pseudomonas aeruginosa Clinical Isolates from Cystic Fibrosis Patients

Pseudomonas aeruginosa is an opportunistic pathogen often involved in airway infections of cystic fibrosis (CF) patients. Its pathogenicity is related to several virulence factors, such as biofilm formation, motility and production of toxins and proteases. The expression of these virulence factors is controlled by quorum sensing (QS). Thus, QS inhibition is considered a novel strategy for the development of antipathogenic compounds acting on specific bacterial virulence programs without affecting bacterial vitality. In this context, cold-adapted marine bacteria living in polar regions represent an untapped reservoir of biodiversity endowed with an interesting chemical repertoire. In this paper, we investigated the biological activity of a supernatant derived from a novel Antarctic bacterium (SN_TAE2020) against specific virulence factors produced by P. aeruginosa strains isolated from FC patients. Our results clearly show a reduction in pyocyanin and protease production in the presence of SN_TAE2020. Finally, SN_TAE2020 was also able to strongly affect swarming and swimming motility for almost all tested strains. Furthermore, the effect of SN_TAE2020 was investigated on biofilm growth and texture, captured by SEM analysis. In consideration of the novel results obtained on clinical strains, polar bacteria might represent potential candidates for the discovery of new compounds limiting P. aeruginosa virulence in CF patients.

Negative Impact of Citral on Susceptibility of Pseudomonas aeruginosa to Antibiotics

Essential oils (EOs) or their components are widely used by inhalation or nebulization to fight mild respiratory bacterial infections. However, their interaction with antibiotics is poorly known. In this study we evaluated the effects of citral, the main component of lemongrass oil, on in vitro susceptibility of Pseudomonas aeruginosa to antibiotics. Exposure of strain PA14 to subinhibitory concentrations of citral increased expression of operons encoding the multidrug efflux systems MexEF-OprN and MexXY/OprM, and bacterial resistance to anti-pseudomonal antibiotics including imipenem (twofold), gentamicin (eightfold), tobramycin (eightfold), ciprofloxacin (twofold), and colistin (≥128-fold). Use of pump deletion mutants showed that in addition to efflux other mechanisms were involved in this citral-induced phenotype. Determination of Zeta potential suggested that citral impairs the cell surface binding of aminoglycosides and colistin used at low concentrations (≤10 μg/mL). Moreover, experiments based on Raman spectroscopy and high-resolution mass spectrometry demonstrated formation of a Schiff base between the aldehyde group of citral and amino-groups of tobramycin and colistin. Chemical synthesis of tobracitryl, the imine compound resulting from condensation of citral and tobramycin, confirmed the loss of antibiotic activity due to adduct formation. Altogether these data point to the potential risk concern of self-medication with EOs containing citral in patients suffering from P. aeruginosa chronic lung infections and being treated with aerosols of aminoglycoside or colistin.

Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training

Evaluating web traffic on a web server is highly critical for web service providers since, without a proper demand forecast, customers could have lengthy waiting times and abandon that website. However, this is a challenging task since it requires making reliable predictions based on the arbitrary nature of human behavior. We introduce an architecture that collects source data and in a supervised way performs the forecasting of the time series of the page views. Based on the Wikipedia page views dataset proposed in a competition by Kaggle in 2017, we created an updated version of it for the years 2018–2020. This dataset is processed and the features and hidden patterns in data are obtained for later designing an advanced version of a recurrent neural network called Long Short-Term Memory. This AI model is distributed training, according to the paradigm called data parallelism and using the Downpour training strategy. Predictions made for the seven dominant languages in the dataset are accurate with loss function and measurement error in reasonable ranges. Despite the fact that the analyzed time series have fairly bad patterns of seasonality and trend, the predictions have been quite good, evidencing that an analysis of the hidden patterns and the features extraction before the design of the AI model enhances the model accuracy. In addition, the improvement of the accuracy of the model with the distributed training is remarkable. Since the task of predicting web traffic in as precise quantities as possible requires large datasets, we designed a forecasting system to be accurate despite having limited data in the dataset. We tested the proposed model on the new Wikipedia page views dataset we created and obtained a highly accurate prediction; actually, the mean absolute error of predictions regarding the original one on average is below 30. This represents a significant step forward in the field of time series prediction for web traffic forecasting.

Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt

Reliance on deep learning techniques has become an important trend in several science domains including biological science, due to its proven efficiency in manipulating big data that are often characterized by their non-linear processes and complicated relationships. In this study, Convolutional Neural Networks (CNN) has been recruited, as one of the deep learning techniques, to be used in classifying and predicting the biological activities of the essential oil-producing plant/s through their chemical compositions. The model is established based on the available chemical composition’s information of a set of endemic Egyptian plants and their biological activities. Another type of machine learning algorithms, Multiclass Neural Network (MNN), has been applied on the same Essential Oils (EO) dataset. This aims to fairly evaluate the performance of the proposed CNN model. The recorded accuracy in the testing process for both CNN and MNN is 98.13% and 81.88%, respectively. Finally, the CNN technique has been adopted as a reliable model for classifying and predicting the bioactivities of the Egyptian EO-containing plants. The overall accuracy for the final prediction process is reported as approximately 97%. Hereby, the proposed deep learning model could be utilized as an efficient model in predicting the bioactivities of, at least Egyptian, EOs-producing plants.

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 different 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.

Graphene Oxide affects Staphylococcus aureus and Pseudomonas aeruginosa dual species biofilm in Lubbock Chronic Wound Biofilm model

Chronic wound management becomes a complex procedure because of the persistence of forming biofilm pathogens that do not respond to antimicrobial treatment. The aim of this paper is to detect the Graphene Oxide-GO effect on Staphylococcus aureus and Pseudomonas aeruginosa dual species wound biofilm in Lubbock Chronic Wound Biofilm-LCWB model. LCWB is a recognized model that mimics the spatial microbial colonization into chronic wounds and reproduces the wound and its clot. Staphylococcus aureus PECHA 10 and P. aeruginosa PECHA 4, are the pathogens used in the study. The GO effect on both in forming and mature biofilms, is detected by the evaluation of the CFU/mg reduction, the cell viability and ultrastructural analysis of the treated LCWBs. Graphene Oxide, at 50 mg/l, shows a significant antibiofilm effect in forming and mature LCWBs. In particular, during the biofilm formation, GO reduces the S. aureus and P. aeruginosa growth of 55.05% ± 4.73 and 44.18% ± 3.91 compared to the control. In mature biofilm, GO affects S. aureus and P. aeruginosa by reducing their growth of 70.24% ± 4.47 and 63.68% ± 17.56, respectively. Images taken by SEM show that GO display a disaggregated microbial effect also disrupting the fibrin network of the wound-like biofilm framework. In conclusion, GO used against microorganisms grown in LCWB, displays a significant inhibitory action resulting in a promising tool for potential application in wound management.

Antimicrobial and antibiofilm activity of the benzoquinone oncocalyxone A

Resistance to antimicrobials is a challenging issue that complicates the treatment of infections caused by bacteria and fungi, thus requiring new therapeutic options. Oncocalyxone A, a benzoquinone obtained from Auxemma oncocalyx (Allem) Taub has several biological effects; however, there is no data on its antimicrobial action. In this study, its antimicrobial and antibiofilm activities were evaluated against bacteria and fungi of clinical interest. Strains of Gram-positive and Gram-negative bacteria, and filamentous fungi and yeasts were selected to determine the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of oncocalyxone A. The antibacterial effect of oncocalyxone A was studied using survival curves, atomic force microscopy (AFM), and the involvement of oxidative stress. We examined the inhibitory action of the molecule on biofilm formation and its hemolytic activity against human erythrocytes. Our results showed that among the strains tested, Staphylococcus epidermidis was highly sensitive to the action of oncocalyxone A, with an MIC of 9.43 μg/mL. In most bacterial strains analyzed, a bacteriostatic effect was observed, though the molecule showed no antifungal activity. Antibiofilm activity was observed against the methicillin-resistant S. aureus bacteria. Additionally, results from atomic force microscopy imaging showed that oncocalyxone A significantly altered bacterial morphology. Further, oncocalyxone A showed no hemolytic activity at concentrations ≥151 μg/mL. Together, our results demonstrate the antibacterial and antibiofilm potential of oncocalyxone A, indicating its therapeutic potential against bacterial resistance.

Incubation with a Complex Orange Essential Oil Leads to Evolved Mutants with Increased Resistance and Tolerance

Emergence of strains with increased resistance/tolerance to natural antimicrobials was evidenced after cyclic exposure to carvacrol, citral, and (+)-limonene oxide. However, no previous studies have reported the development of resistance and tolerance to complex essential oils (EOs). This study seeks to evaluate the occurrence of Staphylococcus aureus strains resistant and tolerant to a complex orange essential oil (OEO) after prolonged cyclic treatments at low concentrations. Phenotypic characterization of evolved strains revealed an increase of minimum inhibitory and bactericidal concentration for OEO, a better growth fitness in presence of OEO, and an enhanced survival to lethal treatments, compared to wild-type strain. However, no significant differences (p > 0.05) in cross-resistance to antibiotics were observed. Mutations in hepT and accA in evolved strains highlight the important role of oxidative stress in the cell response to OEO, as well as the relevance of the cell membrane in the cell response to these natural antimicrobials. This study demonstrates the emergence of S. aureus strains that are resistant and tolerant to EO (Citrus sinensis). This phenomenon should be taken into account to assure the efficacy of natural antimicrobials in the design of food preservation strategies, in cleaning and disinfection protocols, and in clinical applications against resistant bacteria.

In vitro anticariogenic and antibiofilm activities of toothpastes formulated with essential oils

OBJECTIVE

This study aimed to assess the antibacterial and antibiofilm effects of essential oils and herbal toothpastes against bacteria associated with oral diseases.

METHODS

The minimum inhibitory concentration (MIC) and antibiofilm activity of 13 essential oils against Staphylococcus aureus, Streptococcus mutans, Lactobacillus lactis, and Enterococcus faecalis. were determined. Toothpastes were formulated with different concentrations of the most active essential oils, alone and in combination, and evaluated for antibacterial and antibiofilm activities.

RESULTS

Clove, oregano, thyme, and cinnamon essential oils were effective in inhibiting all bacterial strains. The antibacterial activity of cinnamon essential oil was similar to that of the control (0.12 % chlorhexidine gluconate mouthwash). Cinnamon essential oil was a strong inhibitor of S. mutans growth. The antibiofilm activity of clove, oregano, thyme, and cinnamon essential oils at 1, 2, and 4 × MIC against S. mutans did not differ from that of the control. In the hole-plate diffusion assay, 17 out of the 18 tested toothpastes produced an inhibition halo at least half as large as that of the control. Toothpastes containing clove, clove and oregano, or clove, oregano, thyme, and cinnamon essential oils were able to completely disrupt S. mutans biofilms, not differing from the control. Thyme essential oil was found to act synergistically with chlorhexidine against S. mutans.

CONCLUSIONS

The results indicate that clove, oregano, thyme, and cinnamon essential oils may be added to fluoride-free toothpastes to enhance inhibitory effects against bacteria associated with cavities and periodontal disease. Thyme essential oil may increase the efficiency of chlorhexidine-containing dentifrices.

Inhibition of biofilm and virulence properties of Pseudomonas aeruginosa by sub-inhibitory concentrations of aminoglycosides

Aminoglycosides are a commonly used class of antibiotics; however, their application has been discontinued due to the emergence of multi-drug resistance bacterial strains. In the present study, the subinhibitory concentrations (sub-MIC) of several aminoglycosides were determined and tested as an antibiofilm and for their anti-virulence properties against Pseudomonas aeruginosa PAO1, which is an opportunistic foodborne pathogen. P. aeruginosa PAO1 exhibits multiple mechanisms of resistance, including the formation of biofilm and production of several virulence factors, against aminoglycoside antibiotics. The sub-MIC of these antibiotics exhibited biofilm inhibition of P. aeruginosa in alkaline TSB (pH 7.9). Moreover, various concentrations of these aminoglycosides also eradicate the mature biofilm of P. aeruginosa. In the presence of sub-MIC of aminoglycosides, the morphological changes of P. aeruginosa were found to change from rod-shaped to the filamentous, elongated, and streptococcal forms. Similar growth conditions and sub-MIC of aminoglycosides were also found to attenuate several virulence properties of P. aeruginosa PAO1. Molecular docking studies demonstrate that these aminoglycosides possess strong binding properties with the LasR protein, which is a well-characterized quorum-sensing receptor of P. aeruginosa. The present study suggests a new approach to revitalize aminoglycosides as antibiofilm and antivirulence drugs to treat infections caused by pathogenic bacteria.