RESUMO
Background: Enterobacter cloacae (E. cloacae) is one of the most common Enterobacteriaceae causing nosocomial infections. Plasmid-mediated quinolone resistance (PMQR) determinants have been considered recently. This study evaluated the abundance of PMQR genes in strains of E. cloacae obtained from clinical samples in Kermanshah, Iran. Methods: In this descriptive cross-sectional study, after collecting 113 isolates of E. cloacae, their identity was confirmed using specific biochemical tests. After determining their drug resistance patterns using disc diffusion, the phenotypic frequency of extended-spectrum beta-lactamase (ESBL)-producing isolates was measured by the double-disk synergy test (DDST) method. The isolates were examined for the presence of qnrA, qnrB, qnrS, and aac(6')-Ib-cr genes by the polymerase chain reaction (PCR) assay. Results: The antibiotic resistance rate of E. cloacae isolates varied from 9.7% to 60.2%; among them, 78% were multidrug-resistant (MDR). The highest quinolone resistance was observed in ESBL-producing strains of E. cloacae. The frequency of positive isolates for PMQR and ESBL was 79.6% and 57.5%, respectively. The genes aac(6')-ib-cr (70.8%) and qnrB (38.1%) had the highest frequency among other genes. The number of isolates simultaneously carrying 2 and 3 genes was 64 and 5 isolates, respectively. Conclusion: The obtained results indicate a high degree of quinolone resistance among ESBL-producing E. cloacae strains. Nevertheless, there was a significant relationship between the PMQR gene and ESBL-positive isolates. Therefore, special attention should be paid to molecular epidemiological studies on antibiotic resistance to quinolones and beta-lactamases in these strains.
RESUMO
Indole-3-acetic acid (IAA) represents a crucial phytohormone regulating specific tropic responses in plants and functions as a chemical signal between plant hosts and their symbionts. The Actinobacteria strain of AW22 with high IAA production ability was isolated in Algeria for the first time and was characterized as Streptomyces rubrogriseus through chemotaxonomic analysis and 16 S rDNA sequence alignment. The suitable medium for a maximum IAA yield was engineered in vitro and in silico using machine learning-assisted modeling. The primary low-cost feedstocks comprised various concentrations of spent coffee grounds (SCGs) and carob bean grounds (CBGs) extracts. Further, we combined the Box-Behnken design from response surface methodology (BBD-RSM) with artificial neural networks (ANNs) coupled with the genetic algorithm (GA). The critical process parameters screened via Plackett-Burman design (PBD) served as BBD and ANN-GA inputs, with IAA yield as the output variable. Analysis of the putative IAA using thin-layer chromatography (TLC) and (HPLC) revealed Rf values equal to 0.69 and a retention time of 3.711 min, equivalent to the authentic IAA. AW 22 achieved a maximum IAA yield of 188.290 ± 0.38 µg/mL using the process parameters generated by the ANN-GA model, consisting of L-Trp, 0.6%; SCG, 30%; T°, 25.8 °C; and pH 9, after eight days of incubation. An R2 of 99.98%, adding to an MSE of 1.86 × 10-5 at 129 epochs, postulated higher reliability of ANN-GA-approach in predicting responses, compared with BBD-RSM modeling exhibiting an R2 of 76.28%. The validation experiments resulted in a 4.55-fold and 4.46-fold increase in IAA secretion, corresponding to ANN-GA and BBD-RSM models, respectively, confirming the validity of both models.