scholarly journals Essential Oils Biofilm Modulation Activity, Chemical and Machine Learning Analysis—Application on Staphylococcus aureus Isolates from Cystic Fibrosis Patients

2020 ◽  
Vol 21 (23) ◽  
pp. 9258
Author(s):  
Rosanna Papa ◽  
Stefania Garzoli ◽  
Gianluca Vrenna ◽  
Manuela Sabatino ◽  
Filippo Sapienza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cystic Fibrosis ◽  
Staphylococcus Aureus ◽  
Essential Oils ◽  
Machine Learning Algorithms ◽  
Bacterial Biofilm ◽  
Active Components ◽  
Biofilm Growth ◽  
Analysis Application ◽  
Multicomponent Nature

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.

scholarly journals Biofilm Formation on Dental Implant Biomaterials by Staphylococcus aureus Strains Isolated from Patients with Cystic Fibrosis

Materials ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2030
Author(s):  
Anna Minkiewicz-Zochniak ◽  
Sylwia Jarzynka ◽  
Agnieszka Iwańska ◽  
Kamila Strom ◽  
Bartłomiej Iwańczyk ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Staphylococcus Aureus ◽  
Biofilm Formation ◽  
Bacterial Biofilm ◽  
Chromium Alloy ◽  
Systemic Diseases ◽  
Cobalt Chromium ◽  
Bacterial Aggregation ◽  
Cobalt Chromium Alloy

Implants made of ceramic and metallic elements, which are used in dentistry, may either promote or hinder the colonization and adhesion of bacteria to the surface of the biomaterial to varying degrees. The increased interest in the use of dental implants, especially in patients with chronic systemic diseases such as cystic fibrosis (CF), is caused by an increase in disease complications. In this study, we evaluated the differences in the in vitro biofilm formation on the surface of biomaterials commonly used in dentistry (Ti-6Al-4V, cobalt-chromium alloy (CoCr), and zirconia) by Staphylococcus aureus isolated from patients with CF. We demonstrated that S. aureus adherence and growth depends on the type of material used and its surface topography. Weaker bacterial biofilm formation was observed on zirconia surfaces compared to titanium and cobalt-chromium alloy surfaces. Moreover, scanning electron microscopy showed clear differences in bacterial aggregation, depending on the type of biomaterial used. Over the past several decades, S. aureus strains have developed several mechanisms of resistance, especially in patients on chronic antibiotic treatment such as CF. Therefore, the selection of an appropriate implant biomaterial with limited microorganism adhesion characteristics can affect the occurrence and progression of oral cavity infections, particularly in patients with chronic systemic diseases.

scholarly journals Effect of Silver-Doped Phosphate-Based Glasses on Bacterial Biofilm Growth

2008 ◽  
Vol 74 (16) ◽  
pp. 5228-5230 ◽  
Author(s):  
Sabeel P. Valappil ◽  
Jonathan C. Knowles ◽  
Michael Wilson
Keyword(s):  
Staphylococcus Aureus ◽  
Pseudomonas Aeruginosa ◽  
Nosocomial Infections ◽  
Inhibitory Effect ◽  
Bacterial Biofilm ◽  
Growth Inhibitory Effect ◽  
Biofilm Growth ◽  
Growth Inhibitory ◽  
Silver Release

ABSTRACT Silver-containing phosphate-based glasses were found to reduce the growth of Pseudomonas aeruginosa and Staphylococcus aureus biofilms, which are leading causes of nosocomial infections. The rates of glass degradation (1.27 to 1.41 μg·mm−2·h−1) and the corresponding silver release were found to account for the variation in biofilm growth inhibitory effect.

scholarly journals The Staphylococcus aureus Transcriptome during Cystic Fibrosis Lung Infection

mBio ◽  
2019 ◽  
Vol 10 (6) ◽  
Author(s):  
Carolyn B. Ibberson ◽  
Marvin Whiteley
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Cystic Fibrosis ◽  
Staphylococcus Aureus ◽  
Lung Infection ◽  
Human Infection ◽  
Content Type ◽  
Bacterial Physiology ◽  
Laboratory Models

ABSTRACT Laboratory models have been invaluable for the field of microbiology for over 100 years and have provided key insights into core aspects of bacterial physiology such as regulation and metabolism. However, it is important to identify the extent to which these models recapitulate bacterial physiology within a human infection environment. Here, we performed transcriptomics (RNA-seq), focusing on the physiology of the prominent pathogen Staphylococcus aureus in situ in human cystic fibrosis (CF) infection. Through principal-component and hierarchal clustering analyses, we found remarkable conservation in S. aureus gene expression in the CF lung despite differences in the patient clinic, clinical status, age, and therapeutic regimen. We used a machine learning approach to identify an S. aureus transcriptomic signature of 32 genes that can reliably distinguish between S. aureus transcriptomes in the CF lung and in vitro. The majority of these genes were involved in virulence and metabolism and were used to improve a common CF infection model. Collectively, these results advance our knowledge of S. aureus physiology during human CF lung infection and demonstrate how in vitro models can be improved to better capture bacterial physiology in infection. IMPORTANCE Although bacteria have been studied in infection for over 100 years, the majority of these studies have utilized laboratory and animal models that often have unknown relevance to the human infections they are meant to represent. A primary challenge has been to assess bacterial physiology in the human host. To address this challenge, we performed transcriptomics of S. aureus during human cystic fibrosis (CF) lung infection. Using a machine learning framework, we defined a “human CF lung transcriptome signature” that primarily included genes involved in metabolism and virulence. In addition, we were able to apply our findings to improve an in vitro model of CF infection. Understanding bacterial gene expression within human infection is a critical step toward the development of improved laboratory models and new therapeutics.

scholarly journals Anti-Biofilm Inhibitory Synergistic Effects of Combinations of Essential Oils and Antibiotics

Antibiotics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 637 ◽  
Author(s):  
Antonio Rosato ◽  
Sabina Sblano ◽  
Lara Salvagno ◽  
Alessia Carocci ◽  
Maria Lisa Clodoveo ◽  
...  
Keyword(s):  
Essential Oils ◽  
Antimicrobial Agents ◽  
Synergistic Effects ◽  
Bacterial Species ◽  
Bacterial Biofilm ◽  
Mentha Piperita ◽  
Thymus Vulgaris ◽  
Biofilm Growth ◽  
Severe Problem

In recent years, the increase of bacteria antibiotic- resistance has been a severe problem for public health. A useful solution could be to join some phytochemicals naturally present in essential oils (EOs) to the existing antibiotics, with the aim to increase their efficacy in therapies. According to in vitro studies, EOs and their components could show such effects. Among them, we studied the activity of Cinnammonum zeylanicum, Mentha piperita, Origanum vulgare, and Thymus vulgaris EOs on bacterial biofilm and their synergism when used in association with some common antibiotics such as norfloxacin, oxacillin, and gentamicin. The chemical composition of EOs was determined using gas chromatography (GC) coupled with mass spectrometry (MS) techniques. The EOs drug efficacy was evaluated on four different strains of Gram-positive bacteria forming biofilms. The synergistic effects were tested through the chequerboard microdilution method. The association EOs-antibiotics showed a strong destruction of the biofilm growth of the four bacterial species considered. The interaction of norfloxacin with EOs was the most effective in all the tested combinations against the strains object of this study. These preliminary results suggest the formulation of a new generation of antimicrobial agents based on a combination of antimicrobial compounds with different origin.

scholarly journals Staphylococcus aureusBiofilm Growth on Cystic Fibrosis Airway Epithelial Cells Is Enhanced during Respiratory Syncytial Virus Coinfection

mSphere ◽  
2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Megan R. Kiedrowski ◽  
Jordan R. Gaston ◽  
Brian R. Kocak ◽  
Stefanie L. Coburn ◽  
Stella Lee ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Staphylococcus Aureus ◽  
Respiratory Syncytial Virus ◽  
Biofilm Formation ◽  
Airway Epithelial Cells ◽  
Airway Epithelial ◽  
Biofilm Growth ◽  
Content Type ◽  
Syncytial Virus ◽  
Polymicrobial Infections

ABSTRACTStaphylococcus aureusis a major cause of chronic respiratory infection in patients with cystic fibrosis (CF). We recently showed thatPseudomonas aeruginosaexhibits enhanced biofilm formation during respiratory syncytial virus (RSV) coinfection on human CF airway epithelial cells (AECs). The impact of respiratory viruses on other bacterial pathogens during polymicrobial infections in CF remains largely unknown. To investigate ifS. aureusbiofilm growth in the CF airways is impacted by virus coinfection, we evaluatedS. aureusgrowth on CF AECs. Initial studies showed an increase inS. aureusgrowth over 24 h, and microscopy revealed biofilm-like clusters of bacteria on CF AECs. Biofilm growth was enhanced when CF AECs were coinfected with RSV, and this observation was confirmed withS. aureusCF clinical isolates. Apical conditioned medium from RSV-infected cells promotedS. aureusbiofilms in the absence of the host epithelium, suggesting that a secreted factor produced during virus infection benefitsS. aureusbiofilms. Exogenous iron addition did not significantly alter biofilm formation, suggesting that it is not likely the secreted factor. We further characterizedS. aureus-RSV coinfection in our model using dual host-pathogen RNA sequencing, allowing us to observe specific contributions ofS. aureusand RSV to the host response during coinfection. Using the dual host-pathogen RNA sequencing approach, we observed increased availability of nutrients from the host and upregulation ofS. aureusgenes involved in growth, protein translation and export, and amino acid metabolism during RSV coinfection.IMPORTANCEThe airways of individuals with cystic fibrosis (CF) are commonly chronically infected, andStaphylococcus aureusis the dominant bacterial respiratory pathogen in CF children. CF patients also experience frequent respiratory virus infections, and it has been hypothesized that virus coinfection increases the severity ofS. aureuslung infections in CF. We investigated the relationship betweenS. aureusand the CF airway epithelium and observed that coinfection with respiratory syncytial virus (RSV) enhancesS. aureusbiofilm growth. However, iron, which was previously found to be a significant factor influencingPseudomonas aeruginosabiofilms during virus coinfection, plays a minor role inS. aureuscoinfections. Transcriptomic analyses provided new insight into how bacterial and viral pathogens alter host defense and suggest potential pathways by which dampening of host responses to one pathogen may favor persistence of another in the CF airways, highlighting complex interactions occurring between bacteria, viruses, and the host during polymicrobial infections.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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