scholarly journals Fighting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics

2019 ◽  
Author(s):  
Ariane Khaledi ◽  
Aaron Weimann ◽  
Monika Schniederjans ◽  
Ehsaneddin Asgari ◽  
Tzu-Hao Kuo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Pseudomonas Aeruginosa ◽  
Antibiotic Resistance ◽  
Antimicrobial Resistance ◽  
Expression Profiles ◽  
Bacterial Species ◽  
Test System ◽  
Cost Of Care ◽  
Predictive Values

AbstractThe growing importance of antibiotic resistance on clinical outcomes and cost of care underscores the need for optimization of current diagnostics. For a number of bacterial species antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and gene expression profiles, we generated predictive models and identified biomarkers of susceptibility or resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (>0.9) sensitivity and predictive values, where the relative contribution of the different categories of biomarkers strongly depended on the antibiotic. For all drugs except for ciprofloxacin, gene expression information substantially improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine clinical microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

scholarly journals Using Machine Learning to Predict Antimicrobial Resistance of Acinetobacter Baumannii, Klebsiella Pneumoniae and Pseudomonas Aeruginosa Strains

2021 ◽  
Author(s):  
Georgios Feretzakis ◽  
Aikaterini Sakagianni ◽  
Evangelos Loupelis ◽  
Dimitris Kalles ◽  
Maria Martsoukou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pseudomonas Aeruginosa ◽  
Antibiotic Resistance ◽  
Antimicrobial Resistance ◽  
Klebsiella Pneumoniae ◽  
Acinetobacter Baumannii ◽  
Clinical Microbiology Laboratory ◽  
Support Tool ◽  
Basic Characteristics ◽  
Using Data

Hospital-acquired infections, particularly in ICU, are becoming more frequent in recent years, with the most serious of them being Gram-negative bacterial infections. Among them, Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa are considered the most resistant bacteria encountered in ICU and other wards. Given the fact that about 24 hours are usually required to perform common antibiotic resistance tests after the bacteria identification, the use of machine learning techniques could be an additional decision support tool in selecting empirical antibiotic treatment based on the sample type, bacteria, and patient’s basic characteristics. In this article, five machine learning (ML) models were evaluated to predict antimicrobial resistance of Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. We suggest implementing ML techniques to forecast antibiotic resistance using data from the clinical microbiology laboratory, available in the Laboratory Information System (LIS).

scholarly journals Swarming of Pseudomonas aeruginosa Is a Complex Adaptation Leading to Increased Production of Virulence Factors and Antibiotic Resistance

2008 ◽  
Vol 190 (8) ◽  
pp. 2671-2679 ◽  
Author(s):  
Joerg Overhage ◽  
Manjeet Bains ◽  
Michelle D. Brazas ◽  
Robert E. W. Hancock
Keyword(s):  
Gene Expression ◽  
Pseudomonas Aeruginosa ◽  
Antibiotic Resistance ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Leading Edge ◽  
Adaptation Process ◽  
Extracellular Proteases ◽  
Swarming Motility ◽  
Virulence Gene Expression

ABSTRACT In addition to exhibiting swimming and twitching motility, Pseudomonas aeruginosa is able to swarm on semisolid (viscous) surfaces. Recent studies have indicated that swarming is a more complex type of motility influenced by a large number of different genes. To investigate the adaptation process involved in swarming motility, gene expression profiles were analyzed by performing microarrays on bacteria from the leading edge of a swarm zone compared to bacteria growing in identical medium under swimming conditions. Major shifts in gene expression patterns were observed under swarming conditions, including, among others, the overexpression of a large number of virulence-related genes such as those encoding the type III secretion system and its effectors, those encoding extracellular proteases, and those associated with iron transport. In addition, swarming cells exhibited adaptive antibiotic resistance against polymyxin B, gentamicin, and ciprofloxacin compared to what was seen for their planktonic (swimming) counterparts. By analyzing a large subset of up-regulated genes, we were able to show that two virulence genes, lasB and pvdQ, were required for swarming motility. These results clearly favored the conclusion that swarming of P. aeruginosa is a complex adaptation process in response to a viscous environment resulting in a substantial change in virulence gene expression and antibiotic resistance.

Antimicrobial Resistance of Escherichia coli, Enterococci, Pseudomonas aeruginosa, and Staphylococcus aureus from Raw Fish and Seafood Imported into Switzerland

2016 ◽  
Vol 79 (7) ◽  
pp. 1240-1246 ◽  
Author(s):  
RENATE BOSS ◽  
GUDRUN OVERESCH ◽  
ANDREAS BAUMGARTNER
Keyword(s):  
Escherichia Coli ◽  
Staphylococcus Aureus ◽  
Pseudomonas Aeruginosa ◽  
Antibiotic Resistance ◽  
Antimicrobial Resistance ◽  
Antimicrobial Agents ◽  
Bacterial Species ◽  
Health Concern ◽  
E Coli ◽  
And Staphylococcus Aureus

ABSTRACT A total of 44 samples of salmon, pangasius (shark catfish), shrimps, and oysters were tested for the presence of Escherichia coli, enterococci, Pseudomonas aeruginosa, and Staphylococcus aureus, which are indicator organisms commonly used in programs to monitor antibiotic resistance. The isolated bacterial strains, confirmed by matrix-assisted laser desorption ionization time-of-flight mass spectroscopy, were tested against a panel of 29 antimicrobial agents to obtain MICs. Across the four sample types, Enterococcus faecalis (59%) was most common, followed by E. coli (55%), P. aeruginosa (27%), and S. aureus (9%). All bacterial species were resistant to some antibiotics. The highest rates of resistance were in E. faecalis to tetracycline (16%), in E. coli to ciprofloxacin (22%), and in S. aureus to penicillin (56%). Antibiotic resistance was found among all sample types, but salmon and oysters were less burdened than were shrimps and pangasius. Multidrug-resistant (MDR) strains were exclusively found in shrimps and pangasius: 17% of pangasius samples (MDR E. coli and S. aureus) and 64% of shrimps (MDR E. coli, E. faecalis, and S. aureus). Two of these MDR E. coli isolates from shrimps (one from an organic sample) were resistant to seven antimicrobial agents. Based on these findings, E. coli in pangasius, shrimps, and oysters, E. faecalis in pangasius, shrimps, and salmon, and P. aeruginosa in pangasius and shrimps are potential candidates for programs monitoring antimicrobial resistance. Enrichment methods for the detection of MDR bacteria of special public health concern, such as methicillin-resistant S. aureus and E. coli producing extended-spectrum β-lactamases and carbapenemases, should be implemented.

scholarly journals Patient-Specific Predictive Antibiogram in Decision Support for Empiric Antibiotic Treatment

2020 ◽  
Vol 41 (S1) ◽  
pp. s521-s522
Author(s):  
Debarka Sengupta ◽  
Vaibhav Singh ◽  
Seema Singh ◽  
Dinesh Tewari ◽  
Mudit Kapoor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antimicrobial Resistance ◽  
Model Building ◽  
Medical Center ◽  
Bacterial Species ◽  
Model Performance ◽  
The United States ◽  
Patient Specific ◽  
Gradient Boosting ◽  
Comparative Performance

Background: The rising trend of antibiotic resistance imposes a heavy burden on healthcare both clinically and economically (US$55 billion), with 23,000 estimated annual deaths in the United States as well as increased length of stay and morbidity. Machine-learning–based methods have, of late, been used for leveraging patient’s clinical history and demographic information to predict antimicrobial resistance. We developed a machine-learning model ensemble that maximizes the accuracy of such a drug-sensitivity versus resistivity classification system compared to the existing best-practice methods. Methods: We first performed a comprehensive analysis of the association between infecting bacterial species and patient factors, including patient demographics, comorbidities, and certain healthcare-specific features. We leveraged the predictable nature of these complex associations to infer patient-specific antibiotic sensitivities. Various base-learners, including k-NN (k-nearest neighbors) and gradient boosting machine (GBM), were used to train an ensemble model for confident prediction of antimicrobial susceptibilities. Base learner selection and model performance evaluation was performed carefully using a variety of standard metrics, namely accuracy, precision, recall, F1 score, and Cohen κ. Results: For validating the performance on MIMIC-III database harboring deidentified clinical data of 53,423 distinct patient admissions between 2001 and 2012, in the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center in Boston, Massachusetts. From ~11,000 positive cultures, we used 4 major specimen types namely urine, sputum, blood, and pus swab for evaluation of the model performance. Figure 1 shows the receiver operating characteristic (ROC) curves obtained for bloodstream infection cases upon model building and prediction on 70:30 split of the data. We received area under the curve (AUC) values of 0.88, 0.92, 0.92, and 0.94 for urine, sputum, blood, and pus swab samples, respectively. Figure 2 shows the comparative performance of our proposed method as well as some off-the-shelf classification algorithms. Conclusions: Highly accurate, patient-specific predictive antibiogram (PSPA) data can aid clinicians significantly in antibiotic recommendation in ICU, thereby accelerating patient recovery and curbing antimicrobial resistance.Funding: This study was supported by Circle of Life Healthcare Pvt. Ltd.Disclosures: None

PATTERNS OF ANTIMICROBIAL RESISTANCE AMONG MAJOR BACTERIAL PATHOGENS ISOLATED FROM CLINICAL SAMPLES IN TWO TERTIARY’S HOSPITALS, IN SANA'A, YEMEN

2021 ◽  
Author(s):  
Huda Zaid Al-Shami ◽  
Muhamed Ahmed Al-Haimi ◽  
Omar Ahmed Esma’il Al-dossary ◽  
Abeer Abdulmahmood Mohamed Nasher ◽  
Mohammed Mohammed Ali Al-Najhi ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Antibiotic Resistance ◽  
Antimicrobial Resistance ◽  
Peer Review ◽  
Bacterial Pathogens ◽  
University Hospital ◽  
Clinical Samples ◽  
Isolation And Identification ◽  
Hemolytic Streptococcus ◽  
Study Results

Background and objectives: At the present time, antimicrobial resistance (AMR) is a major public health hazard, with antimicrobial resistance bacteria increasing exponentially. This study estimates the epidemiological profiles and antimicrobial resistance of Gram-positive bacteria (GPB) and Gram-negative bacteria (GNB)  isolated from clinical samples among patients admitted to two University hospitals in Sana'a city for one year (2019). Methods: This was a retrospective study of clinical samples of patients collected from January 1, 2019 to December 30, 2019. All samples were appraised to determine presence of infectious agents using standard methods for isolation and identification of bacteria and yeasts from clinical samples of patients admitted to Al-Gumhouri University Hospital and Al-Kuwait University Hospital in Sana'a city. Antibiotic resistance was done using Kirby-Bauer disc diffusion methods. Results:  2,931 different pathogenic bacteria were detected from 24,690 different clinical specimens. The samples had an overall detection rate of 11.9% (2931/24,690). Among the bacterial pathogens isolated from clinical samples, 52.4% (n=1536) had GPB and 41.2% (n=1207) had GNB. The predominant GNB isolates were E.coli (22.04%), Klebsiella spp (6.03%), Pseudomonas aeruginosa (7.1%), Acinetobacter baumannii (1.46%), Enterobacter spp. (1.09%), Citrobacter spp. (1.16%), respectively. Among the GPB, S.aureus was the most common (26.3%), Coagulase-negative Staphylococcus (8.1%), Non-hemolytic Streptococcus (9.1%), Other alpha-hemolytic Streptococcus (3.9%), Streptococcus pyogenes (1.9%), and Streptococcus pneumoniae (0.5% ). A high rate of antibiotic resistance was recorded for sulfamethoxazole/trimethoprim (85.5%), ceftazidime (81.07%), ampicillin (70.4%), cefuroxime (66.4%). Conclusions:  The current study results revealed that the rate of resistance between GNB and GPB is associated with the incidence of different infections in patients attending two major tertiary hospitals in Sana'a city is very high. These results indicate ongoing screening and follow-up programs to detect antibiotic resistance, and also suggest the development of antimicrobial stewardship programs in Sana'a, Yemen.                     Peer Review History: Received: 9 September 2021; Revised: 11 October; Accepted: 23 October, Available online: 15 November 2021 Academic Editor:  Dr. A.A. Mgbahurike, University of Port Harcourt, Nigeria, [email protected] UJPR follows the most transparent and toughest ‘Advanced OPEN peer review’ system. The identity of the authors and, reviewers will be known to each other. This transparent process will help to eradicate any possible malicious/purposeful interference by any person (publishing staff, reviewer, editor, author, etc) during peer review. As a result of this unique system, all reviewers will get their due recognition and respect, once their names are published in the papers. We expect that, by publishing peer review reports with published papers, will be helpful to many authors for drafting their article according to the specifications. Auhors will remove any error of their article and they will improve their article(s) according to the previous reports displayed with published article(s). The main purpose of it is ‘to improve the quality of a candidate manuscript’. Our reviewers check the ‘strength and weakness of a manuscript honestly’. There will increase in the perfection, and transparency.  Received file:                Reviewer's Comments: Average Peer review marks at initial stage: 6.0/10 Average Peer review marks at publication stage: 7.5/10 Reviewers: Rima Benatoui, Laboratory of Applied Neuroendocrinology, Department of Biology, Faculty of Science, Badji Mokhtar University Annaba, BP12 E L Hadjar–Algeria, [email protected] Dr. Wadhah Hassan Ali Edrees, Hajja University, Yemen, [email protected] Rola Jadallah, Arab American University, Palestine, [email protected] Similar Articles: PREVALENCE OF PSEUDOMONAS AERUGINOSA (P. AERUGINOSA) AND ANTIMICROBIAL SUSCEPTIBILITY PATTERNS AT A PRIVATE HOSPITAL IN SANA'A, YEMEN EVALUATION OF ANTIBACTERIAL RESISTANCE OF BIOFILM FORMS OF AVIAN SALMONELLA GALLINARUM TO FLUOROQUINOLONES

scholarly journals Integrative machine learning analysis of multiple gene expression profiles in cervical cancer

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5285 ◽  
Author(s):  
Mei Sze Tan ◽  
Siow-Wee Chang ◽  
Phaik Leng Cheah ◽  
Hwa Jen Yap
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Cervical Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Hpv Infection ◽  
Gene Set Enrichment Analysis ◽  
Multiple Gene ◽  
Cervical Cancers ◽  
Learning Analysis

Although most of the cervical cancer cases are reported to be closely related to the Human Papillomavirus (HPV) infection, there is a need to study genes that stand up differentially in the final actualization of cervical cancers following HPV infection. In this study, we proposed an integrative machine learning approach to analyse multiple gene expression profiles in cervical cancer in order to identify a set of genetic markers that are associated with and may eventually aid in the diagnosis or prognosis of cervical cancers. The proposed integrative analysis is composed of three steps: namely, (i) gene expression analysis of individual dataset; (ii) meta-analysis of multiple datasets; and (iii) feature selection and machine learning analysis. As a result, 21 gene expressions were identified through the integrative machine learning analysis which including seven supervised and one unsupervised methods. A functional analysis with GSEA (Gene Set Enrichment Analysis) was performed on the selected 21-gene expression set and showed significant enrichment in a nine-potential gene expression signature, namely PEG3, SPON1, BTD and RPLP2 (upregulated genes) and PRDX3, COPB2, LSM3, SLC5A3 and AS1B (downregulated genes).

scholarly journals Antimicrobial Resistance and Biofilm Formation of Pseudomonas aeruginosa

10.3823/846 ◽  
2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Abdelraouf A Elmanama ◽  
Suhaila Al-Sheboul ◽  
Renad I Abu-Dan
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Antibiotic Resistance ◽  
Antimicrobial Resistance ◽  
Quorum Sensing ◽  
Bacterial Adhesion ◽  
Biofilm Formation ◽  
Bacterial Infections ◽  
Developmental Stages ◽  
Infection Site ◽  
Major Virulence Factor

Abstract Pseudomonas aeruginosa threatens patient’s care. It is considered as the most complicated health care associated pathogen to be eliminated from infection site. The biofilm forming ability of P. aeruginosa, being a major virulence factor for most pathogenic microorganism, protects it from host immunity and contribute to antibiotic resistance of this organism. It is estimated that about 80% of infectious diseases are due to biofilm mode of growth. Biofilm forming ability of bacteria imparts antimicrobial resistance that leads to many persistent and chronic bacterial infections. The world is becoming increasingly under the threat of entering the “post-antibiotic era”, an era in which the rate of death from bacterial infections is higher than from cancer. This review focus on P. aeruginosa biofilm forming ability; definition, developmental stages, and significance. In addition, the quorum sensing and the antibiotic resistance of this pathogen is discussed. Keywords: Biofilm; bacterial adhesion; Pseudomonas aeruginosa; antimicrobial resistance; quorum sensing.

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