Introduction. Community-acquired urinary tract infections (CAUTIs) are usually treated empirically. Geographical variations in etiologic agents and their antibiotic sensitivity patterns are common. Knowledge of antibiotic resistance trends is important for improving evidence-based recommendations for empirical treatment of UTIs. Our aim was to determine the major bacterial etiologies of CAUTIs and their antibiotic resistance patterns in a cosmopolitan area of Cameroon for comparison with prescription practices of local physicians. Methods. We performed a cross-sectional descriptive study at two main hospitals in Yaoundé, collecting a clean-catch mid-stream urine sample from 92 patients having a clinical diagnosis of UTI. The empirical antibiotherapy was noted, and identification of bacterial species was done on CLED agar; antibiotic susceptibility testing was performed using the Kirby-Bauer disc diffusion method. Results. A total of 55 patients had samples positive for a UTI. Ciprofloxacin and amoxicillin/clavulanic acid were the most empirically prescribed antibiotics (30.9% and 23.6%, resp.); bacterial isolates showed high prevalence of resistance to both compounds. Escherichia coli (50.9%) was the most common pathogen, followed by Klebsiella pneumoniae (16.4%). Prevalence of resistance for ciprofloxacin was higher compared to newer quinolones. Conclusions. E. coli and K. pneumoniae were the predominant bacterial etiologies; the prevalence of resistance to commonly prescribed antibiotics was high.

IntroductionMonitoring the prevalence of nasal carriage of multiple drug resistance (MDR) Staphylococcus aureus (SA) strains in hospital personnel is essential. These strains when transmitted from hospital personnel to patients with already weakened immune states or in-built medical devices, may limit the latter's treatment options. This study aimed at assessing the potential exposure of patients to these MDR SA in a resource-limited hospital setting by assessing the prevalence and relationship between antimicrobial susceptibility and biofilm forming capacity of SA isolates from hospital personnel.MethodsA total of 59 bacteria isolates phenotypically identified as Staphylococcus aureus obtained from medical (39) and non-medical personnel (20) in Yaounde were used in the study. Multiple drug resistance defined as resistance to four or more of twelve locally used antibiotics were determined by Kirby Bauer disc diffusion technique whereas quantification of biofilm production was by the microtitre plate method.ResultsAmong the 59 SA isolates, the prevalence of MDR was 50.9%. Among medical personnel 48.7% had MDR as against 55.9% for non-medical personnel (p-value=0.648). The overall percentage of weak biofilm producers was 35.6%. Although the prevalence of weak biofilm formers was higher in isolates from non-medical personnel (40%) than medical personnel (33.3%) the difference was not statistically significant (p-value= 0.246). Slightly less than half (42.9%) of the weak biofilm producers were MDR.ConclusionConsidering the high rates of MDR and that slightly less than half of biofilm formers were MDR, these trends need to be monitored regularly among hospital personnel in Yaounde.

Pseudomonas aeruginosa has been implicated in a wide range of post-operation wound and lung infections. A wide range of acquired resistance and virulence markers indicate surviving strategy of P. aeruginosa. Complete-genome analysis has been identified as efficient approach towards understanding the pathogenicity of this organism. This study was designed to sequence the entire genome of P. aeruginosa UY1PSABAL and UY1PSABAL2; determine drug-resistance profiles and virulence factors of the isolates; assess factors that contribute toward stability of the genomes; and thereafter determine evolutionary relationships between the strains and other isolates from similar sources. The genomes of the MDR P. aeruginosa UY1PSABAL and UY1PSABAL2 were sequenced on the Illumina Miseq platform. The raw sequenced reads were assessed for quality using FastQC v.0.11.5 and filtered for low quality reads and adapter regions using Trimmomatic v.0.36. The de novo genome assembly was made with SPAdes v.3.13 and annotated using Prokka v.2.1.1 annotation pipeline; Rapid Annotation using Subsytems Technology (RAST) server v.2.0; and PATRIC annotation tool v.3.6.2. Antimicrobial resistance genes and virulence determinants were searched through the functional annotation data generated from Prokka, RAST and PATRIC annotation pipelines; In addition to ResFinder and Comprehensive Antibiotic Resistance Database (CARD) which were employed to determine resistance genes. The PHAge Search Tool Enhanced Release (PHASTER) web server was used for the rapid identification and annotation of prophage sequences within bacterial genome. Predictive secondary metabolites were identified with AntiSMASH v.5.0. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and cas genes regions were also investigated with the CRISPRone and CRISPRFinder server. The genome sizes of 7.0 and 6.4 Mb were