Resistance of Acinetobacter baumannii to multiple clinically important antimicrobials has increased to very high rates in Greece, rendering most of them obsolete. The aim of this study was to determine the molecular epidemiology and susceptibilities of A. baumannii isolates collected from different hospitals across Greece. Single-patient A. baumannii strains isolated from blood cultures (n = 271), from 19 hospitals, in a 6-month period (November 2020-April 2021) were subjected to minimum inhibitory concentration determination and molecular testing for carbapenemase, 16S rRNA methyltransferase and mcr gene detection and epidemiological evaluation. 98.9% of all isolates produced carbapenemase OXA-23. The vast majority (91.8%) of OXA-23 producers harbored the armA and were assigned mainly (94.3%) to sequence group G1, corresponding to IC II. Apramycin (EBL-1003) was the most active agent inhibiting 100% of the isolates at ≤16 mg/L, followed by cefiderocol which was active against at least 86% of them. Minocycline, colistin and ampicillin-sulbactam exhibited only sparse activity (S <19%), while eravacycline was 8- and 2-fold more active than minocycline and tigecycline respectively, by comparison of their MIC50/90 values. OXA-23-ArmA producing A. baumannii of international clone II appears to be the prevailing epidemiological type of this organism in Greece. Cefiderocol could provide a useful alternative for difficult to treat Gram-negative infections, while apramycin (EBL-1003), the structurally unique aminoglycoside currently in clinical development, may represent a highly promising agent against multi-drug resistant A. baumanni infections, due to its high susceptibility rates and low toxicity.

UNASSIGNED: Acinetobacter baumannii (A. baumannii) is one of the most important nosocomial pathogens responsible for a wide range of infections.
UNASSIGNED: This study aimed to investigate the existence of the plasmidic genes encoding for aminoglycoside modifying enzymes (AMEs), 16S rRNA methyltransferases (RMT), and the altered dihydropetroate synthase (DHPS) encoded by the sul1 gene among A. baumannii clinical isolates collected from Taif, Kingdom of Saudi Arabia (KSA). The mutations in aac(6\')-Ib and sul1 genes were also investigated.
UNASSIGNED: Forty A. baumannii clinical isolates were investigated for their susceptibility to ten antibiotics. The plasmid DNA was extracted and screened for nine genes encoding for aminoglycoside resistance in addition to the sul1 gene. The clonal relatedness was determined by random amplified polymorphic DNA (RAPD)-PCR. Mutation in aac(6\')-Ib and the sul1 genes were detected by capillary electrophoresis sequencing (CES).
UNASSIGNED: All isolates were A. baumannii in which 42.5% of them exhibited a high level of aminoglycoside resistance (HLAR). The most prevalent AMEs and RMT encoding genes were aph(3\')-VI, the two aac(6\') gene variants [aac(6\')-Ib and aac(6\')-SL], ant(3\'\')-I, and armA in which 90%, 87.5%, 85%, and 45% of isolates tested positive, respectively. The other investigated aminoglycoside resistant encoding genes, namely aac(3)-II, aac(6\')-II, and rmtB, were not detected. Only 15% of isolates harbored the sul1 gene. RAPD-PCR classified the 40 isolates into three clusters in which cluster II was the main cluster. DNA sequencing revealed that 34.29% (12/35) of isolates tested positive for aac(6\')-Ib were found to harbor a common missense mutation in position 102 indicating a novel allelic variant named aac(6\')-SL. Also, DNA sequencing revealed three missense mutations in the sul1 gene.
UNASSIGNED: This is the first Saudi study to investigate the plasmid borne aminoglycoside and sulfonamide resistance genes among A. baumannii clinical isolates. A novel allelic variant for aac(6\')-Ib was detected in addition to novel mutations in the sul1 gene.

UNASSIGNED: The aminoglycosides are widely used for the therapeutic management of infections caused by gram-negative bacteria, including the Acinetobacter baumannii strains. However, the resistance to the members of the aminoglycoside family, such as amikacin, gentamicin, and tobramycin, is increasingly being common among the clinical isolates.
UNASSIGNED: This study aimed to investigate the presence of 16SrRNA methylases and aminoglycoside modifying enzymes (AMEs) genes among aminoglycoside resistant A. baumannii isolates and to study the genetic diversity of the clinical population of A. baumannii in local hospitals.
UNASSIGNED: The 143 A. baumannii clinical strains were analyzed for antimicrobial susceptibility, genetic screening for enzymes conferring aminoglycosides resistance followed by the multilocus sequence typing.
UNASSIGNED: The 133/143 (93%) isolates were non-susceptible to at least one of the tested aminoglycosides, including amikacin, gentamicin, and tobramycin. The MIC distribution has shown that 87.486.7% strains were resistant to amikacin and gentamicin, respectively. The aphA6, aadB, aacC1, and aphA1 were found in 74.1%, 59.4%, 16.1%, and 11.2% isolates, respectively, whereas the armA was found in 28% of the strains having a higher MIC value (MIC; ≥256µg/mL). The MLST data have shown that the ST589 and ST2 were the most common STs and corresponded to 51 (35.7%) and 38 (26.6%) isolates, respectively, and few of the isolates corresponding to these STs were found to harbor the armA gene with a variable genotypic profile for AMEs.
UNASSIGNED: The study has reported the incidence of various enzymes conferring aminoglycoside resistance among the A. baumannii clones for the first time from Pakistan. The findings suggest the possibility of transmission of aminoglycoside resistance determinants through the lateral gene transfer as well as clonal dissemination.

Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field.