scholarly journals MECHANISMS OF ANTIMICROBIAL RESISTANCE SPECIFIC FOR PSEUDOMONAS AERUGINOSA AND ACINETOBACTER BAUMANNII

InterConf ◽  
2021 ◽  
pp. 266-274
Author(s):  
Greta Balan ◽  
Emilia Behta ◽  
Olga Brînza ◽  
Livia Ţapu ◽  
Olga Burduniuc
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Antimicrobial Resistance ◽  
Acinetobacter Baumannii ◽  
Single Agent ◽  
Mechanisms Of Resistance ◽  
Development Of Resistance ◽  
Resistance Patterns

The exposure of bacteria to antibiotics has led to the development of resistance againstevery single agent utilized. Through a understanding the mechanisms of resistance, the clinician can better comprehend and predict resistance patterns even to antibioticsnot reported on the antibiogram, and subsequently select the most appropriate antibiotic for the pathogen in question.

scholarly journals Detection of extended spectrum beta-lactamase genes in Pseudomonas aeruginosa isolated from patients in rural Eastern Cape Province, South Africa

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mojisola C. Hosu ◽  
Sandeep D. Vasaikar ◽  
Grace E. Okuthe ◽  
Teke Apalata
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Antimicrobial Resistance ◽  
Multidrug Resistant ◽  
High Rate ◽  
Infection Prevention And Control ◽  
Beta Lactamase ◽  
Eastern Cape ◽  
Extended Spectrum Beta Lactamase ◽  
Extended Spectrum ◽  
Resistance Patterns

AbstractThe proliferation of extended spectrum beta-lactamase (ESBL) producing Pseudomonas aeruginosa represent a major public health threat. In this study, we evaluated the antimicrobial resistance patterns of P. aeruginosa strains and characterized the ESBLs and Metallo- β-lactamases (MBL) produced. Strains of P. aeruginosa cultured from patients who attended Nelson Mandela Academic Hospital and other clinics in the four district municipalities of the Eastern Cape between August 2017 and May 2019 were identified; antimicrobial susceptibility testing was carried out against thirteen clinically relevant antibiotics using the BioMérieux VITEK 2 and confirmed by Beckman autoSCAN-4 System. Real-time PCR was done using Roche Light Cycler 2.0 to detect the presence of ESBLs; blaSHV, blaTEM and blaCTX-M genes; and MBLs; blaIMP, blaVIM. Strains of P. aeruginosa demonstrated resistance to wide-ranging clinically relevant antibiotics including piperacillin (64.2%), followed by aztreonam (57.8%), cefepime (51.5%), ceftazidime (51.0%), piperacillin/tazobactam (50.5%), and imipenem (46.6%). A total of 75 (36.8%) multidrug-resistant (MDR) strains were observed of the total pool of isolates. The blaTEM, blaSHV and blaCTX-M was detected in 79.3%, 69.5% and 31.7% isolates (n = 82), respectively. The blaIMP was detected in 1.25% while no blaVIM was detected in any of the strains tested. The study showed a high rate of MDR P. aeruginosa in our setting. The vast majority of these resistant strains carried blaTEM and blaSHV genes. Continuous monitoring of antimicrobial resistance and strict compliance towards infection prevention and control practices are the best defence against spread of MDR P. aeruginosa.

scholarly journals Antimicrobial Resistance Patterns and Prevalence of blaPER-1 and blaVEB-1 Genes Among ESBL-producing Pseudomonas aeruginosa Isolates in West of Iran

2014 ◽  
Vol 7 (1) ◽  
Author(s):  
Mohammad Yousef Alikhani ◽  
Zahra Karimi Tabar ◽  
Fatemeh Mihani ◽  
Enayat Kalantar ◽  
Pegman Karami ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Antimicrobial Resistance ◽  
Resistance Patterns

scholarly journals Molecular Detection of blaOXA-type Carbapenemase Genes and Antimicrobial Resistance Patterns among Clinical Isolates of Acinetobacter baumannii

2021 ◽  
Author(s):  
Maghsoud Kafshnouchi ◽  
Marzieh Safari ◽  
Amir Khodavirdipour ◽  
Abbas Bahador ◽  
Seyed Hamid Hashemi ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Acinetobacter Baumannii ◽  
Dominant Gene ◽  
Diffusion Method ◽  
Phylogenetic Tree Analysis ◽  
Disk Diffusion Method ◽  
Treatment Regimens ◽  
Class D ◽  
Resistance Patterns ◽  
New Treatment

Abstract Acinetobacter baumannii is a bacterium found in most places, especially in clinics and hospitals, and an important agent of nosocomial infections. The presence of class D enzymes such as OXA-type carbapenemases in A. baumannii is proven to have a key function in resistance to carbapenem. The aim of the current study is to determine the blaOXA-type carbapenemase genes and antimicrobial resistance among clinically isolated samples of A. baumannii. We assessed 100 clinically isolated specimens of A. baumannii from patients in intensive care units of educational hospitals of Hamadan, West of Iran. The A. baumannii isolates' susceptibility to antibiotics was performed employing disk diffusion method. Multiplex polymerase chain reaction was used to identify the blaOXA-24-like , blaOXA-23-like , blaOXA-58-like , and blaOXA-51-like genes. The blaOXA-23-like , blaOXA-24-like , and blaOXA-58-like genes' prevalence were found to be 84, 58, and 3%, respectively. The highest coexistence of the genes was for blaOXA-51/23 (84%) followed by blaOXA-51/24-like (58%). The blaOXA-51/23- like pattern of genes is a sort of dominant gene in resistance in A. baumannii from Hamadan hospitals. The highest resistance to piperacillin (83%) and ciprofloxacin (81%) has been observed in positive isolates of blaOXA-23-like . The A. baumannii isolates with blaOXA-58-like genes did not show much resistance to antibiotics. Based on the results of the phylogenetic tree analysis, all isolates have shown a high degree of similarity. This study showed the high frequency of OXA-type carbapenemase genes among A. baumannii isolates from Hamadan hospitals, Iran. Thus, applying an appropriate strategy to limit the spreading of these strains and also performing new treatment regimens are necessary.

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).

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