Nowadays, increasing extended-spectrum β-lactamase (ESBL)-producing bacteria have become a global concern because of inducing resistance toward most of the antimicrobial classes and making the treatment difficult. In order to achieve an appropriate treatment option, identification of the prevalent species which generate ESBL as well as their antibiotic susceptibility pattern is essential worldwide. Hence, this study aimed to investigate the prevalence of ESBL-producing bacteria and assess their drug susceptibility in Fardis Town, Iran. A total of 21,604 urine samples collected from patients suspected to have urinary tract infection (UTI) were processed in the current study. The antimicrobial susceptibility of the isolates was tested by the disk diffusion method. The ESBL producing bacteria were determined by Double Disc Synergy Test (DDST) procedure. Bacterial growth was detected in 1408 (6.52%) cases. The most common bacterial strains causing UTI were found E. coli (72.16%), followed by K. pneumoniae (10.3%) and S. agalactiae (5.7%). Overall, 398 (28.26%) were ESBL producer. The highest ESBL production was observed in E. coli, followed by Klebsiella species. ESBL producers revealed a higher level of antibiotic resistance compared with non-ESBLs. In conclusion, ESBL production in uropathogens was relatively high. Carbapenems and Aminoglycosides were confirmed as the most effective treatment options for these bacteria.

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Background The importance of successful implementation of e-learning, especially since the emergence of the Covid-19 pandemic, has become increasingly apparent to universities. Thus, identifying the effective factors in adopting e-learning in the Covid-19 pandemic is crucial. This study was conducted to identify determining factors in adopting E-learning in healthcare. Method This was a descriptive-analytical study in which 143 faculty members from Iran were randomly selected. The faculty members’ intentions, concerning the adoption of e-learning, were assessed by the conceptual path model of integration of unified theory of acceptance and use of technology (UTAUT) and The Task-Technology Fit (TTF). Results The results showed that the combination of the two classical theories, UTAUT and TTF, was an appropriate model to explain faculty members’ intention in adopting e-learning. Moreover, the findings showed that technology and task characteristics, task- technology fit, social influences, effort expectancy, performance expectancy and facilitating conditions had direct and significant effect on e-learning adoption. Conclusion By presenting a conceptual path model to elucidate users’ behavior in adopting e-learning, this study investigated and identified the key determining factors in adopting e-learning. The findings of the present study can contribute to the design and implementation of e-learning by practitioners, policy makers, and curriculum designers.

Background From the beginning of the COVID-19 pandemic, the development of infrastructures to record, collect and report COVID-19‏ ‏data has become a fundamental necessity in the world. The disease registry system can help build an infrastructure to collect data systematically. The study aimed to design a minimum data set for the COVID-19 registry system. Methods A qualitative study to design an MDS for the COVID-19 registry system was performed in five phases at Ahvaz University of Medical Sciences in Khuzestan Province in southwestern Iran, 2020–2021. In the first phase, assessing the information requirements was performed for the COVID-19 registry system. Data elements were identified in the second phase. In the third phase, the MDS was selected, and in the four phases, the COVID-19 registry system was implemented as a pilot study to test the MDS. Finally, based on the experiences gained from the COVID-19 registry system implementation, the MDS were evaluated, and corrections were made. Results MDS of the COVID-19 registry system contains eight top groups including administrative (34 data elements), disease exposure (61 data elements), medical history and physical examination (138 data elements), findings of clinical diagnostic tests (101 data elements), disease progress and outcome of treatment (55 data elements), medical diagnosis and cause of death (12 data elements), follow-up (14 data elements), and COVID-19 vaccination (19 data elements) data, respectively. Conclusion Creating a standard and comprehensive MDS can help to design any national data dictionary for COVID-19 and improve the quality of COVID-19 data.

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