Objective: To determine the carbapenemases in carbapenem-resistant Acinetobacter species.
Methods: This descriptive, cross-sectional study was carried out at the Jinnah Postgraduate Medical Centre, Karachi, from March to December 2014, and comprised Acinetobacter species isolated from the clinical specimen collected from hospitalised neonates. The screening for carbapenem resistance was performed by meropenem and imipenem discs, and minimum inhibitory concentrations. SPSS 16 was used for data analysis. .
Results: A total of 100 Acinetobacter isolates were included. The patients' age ranged from 1-28 days. The main species 95(95%) was Acinetobacter calcoaceticus-baumannii complex, followed by Acinetobacter lwoffii 5(5%). The overall resistance to carbapenem was 95(95%); it was higher 100 (100%) in Acinetobacter lwoffii in comparison to Acinetobacter calcoaceticus-baumannii complex 90 (94.7%). Phenotypic characterisation revealed that 89 (93.6%) of both the species were class D carbapenemase producers, 2 (2.1%) were metallo-b-lactamases and 4 (4.2%) were non-producers.
Conclusions: Among carbapenem-resistant Acinetobacter species, the class D carbapenemases were the main mode of resistance to carbapenems.
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Heliyon
January 2025
ANSES - Université de Lyon, Unité Antibiorésistance et Virulence Bactériennes, Lyon, France.
causes hospital-acquired infections in human patients with compromised immune system. Strains associated to nosocomial infections are often resistant to carbapenems and belong to few international clones (IC1-11). .
View Article and Find Full Text PDFUnlabelled: As sequencing costs decrease, short-read and long-read technologies are indispensable tools for uncovering the genetic drivers behind bacterial pathogen resistance. This study explores the differences between the use of short-read (Illumina) and long-read (Oxford Nanopore Technologies, ONT) sequencing in detecting antimicrobial resistance (AMR) genes in ESKAPE pathogens ( and ). Utilizing a dataset of 1,385 whole genome sequences and applying commonly used bioinformatic methods in bacterial genomics, we assessed the differences in genomic completeness, pangenome structure, and AMR gene and point mutation identification.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China.
Mariculture is known to harbor antibiotic resistance genes (ARGs), which can be released into marine ecosystems via oceanic farming ponds, posing a public health concern. In this study, metagenomic sequencing was used to decipher the profiles of quinolone-resistant microbiomes and the mechanisms of quinolone resistance in sediment, seawater, and fish gill samples from five mariculture ponds. Residues of both veterinary-specific (enrofloxacin and sarafloxacin) and prohibited quinolones (ofloxacin, ciprofloxacin, pefloxacin, norfloxacin, and lomefloxacin) were detected.
View Article and Find Full Text PDFJ Glob Antimicrob Resist
January 2025
Infection Program, Monash Biomedicine Discovery Institute, Department of Microbiology, Monash University, Clayton Victoria, Australia; Centre to Impact AMR, Monash University, Clayton, Victoria, Australia; Department of Infectious Diseases, Alfred Health and School of Translational Medicine, Monash University, Melbourne, Victoria, Australia. Electronic address:
Objective: The IMP-4 carbapenemase is an endemic cause of carbapenem resistance in the Asia-Pacific region. Our aim was to determine the dissemination mechanism of the bla gene.
Methods: Twelve representative Australian IMP-4 clinical isolates from The Alfred Hospital, were characterised using antimicrobial susceptibility testing and genome and plasmid assemblies analysed.
Nat Microbiol
January 2025
School of Environmental and Chemical Engineering, Shanghai University, Shanghai, China.
Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-based, explainable deep learning model, EvoGradient, that predicts the potency of antimicrobial peptides (AMPs) and virtually modifies peptide sequences to produce more potent AMPs, akin to in silico directed evolution. We applied this model to peptides encoded in low-abundance human oral bacteria, resulting in the virtual evolution of 32 peptides into potent AMPs.
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