Publications by authors named "A P Carvalho-Assef"

Introduction: contributes significantly to the global issue of multidrug-resistant (MDR) nosocomial infections. Often, these strains demonstrate resistance to carbapenems (MDR-CRAB), the first-line treatment for infections instigated by MDR . Our study focused on the antimicrobial susceptibility and genomic sequences related to plasmids from 12 clinical isolates of that carry both the blaOXA-58 and carbapenemase genes.

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Article Synopsis
  • Carbapenemase production is a significant global health concern, and analyzing antimicrobial resistance (AMR) data is essential for effective public health policies, as showcased in this study focused on Brazil's AMR Surveillance Network.
  • The study evaluated carbapenemase detection rates in Brazilian hospitals from 2015 to 2022, finding trends such as a notable increase in blaNDM detection and a decrease in blaKPC for Enterobacterales, particularly after the onset of the COVID-19 pandemic.
  • Overall, the findings highlight the resilience of the AMR Surveillance Network and indicate a concerning shift in carbapenemase profiles, with blaNDM becoming increasingly prevalent.
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Article Synopsis
  • Carbapenems are crucial last-resort antibiotics for treating infections from multidrug-resistant Gram-negative bacteria, but resistance through mechanisms like OprD loss and carbapenemases is a growing global concern.
  • A study in Brazil tested the effectiveness of three phenotypic tests—Carba NP, Blue Carba, and mCIM/eCIM—to detect carbapenemase-producing Pseudomonas species, using 127 clinical isolates.
  • Results showed that Carba NP had the highest sensitivity and successfully differentiated between carbapenemases types, while Blue Carba struggled with class B detection, highlighting the need for improved and more reliable resistance detection methods.
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Background: Healthcare-associated infections due to multidrug-resistant (MDR) bacteria such as Pseudomonas aeruginosa are significant public health issues worldwide. A system biology approach can help understand bacterial behaviour and provide novel ways to identify potential therapeutic targets and develop new drugs. Gene regulatory networks (GRN) are examples of in silico representation of interaction between regulatory genes and their targets.

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Antibacterial drugs are a widely used drug class due to the frequency of infectious diseases globally. Risks knowledge should ground these medicines' selection. Data mining in large databases is essential to identify early safety signals and to support pharmacovigilance systems.

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