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Background: Helicobacter pylori antibiotic resistance has increased worldwide and affects the effectiveness of current therapies. The recommended first-line empiric treatment should be tailored to the local clarithromycin resistance rate. This study aimed to determine the pediatric patient profile and rate of clarithromycin resistance for patients diagnosed with Helicobacter pylori by gastric biopsy. Methods: We studied 84 positive gastric samples for Helicobacter pylori. Positive results were confirmed by a rapid urease test and histopathological examination, with the type of gastritis established according to the Sydney System. Gastric biopsy samples were stored in RNA saver. Clarithromycin resistance was determined by a real-time polymerase chain reaction-based molecular assay after RNA-DNA extraction. Results: Of the 84 biopsy samples analyzed, 35 (41.6%) were resistant to clarithromycin. Clarithromycin resistance was found mainly in girls (80%) with a mean age of 15 years (range 6–17 years). The history of prior exposure to clarithromycin was 91.6%. The concordance between the histopathological examination and the PCR test was 100%. Conclusions: One in 2.4 children infected with Helicobacter pylori had a strain resistant to clarithromycin. This resistant strain may be a reason for treatment failure in Romanian children, yet this is uninvestigated. The high rate of bacterial resistance to this antibiotic among children indicates the need for susceptibility testing before therapy. [ABSTRACT FROM AUTHOR]

Nowadays, REpresentational State Transfer Application Programming Interfaces (REST APIs) are widely used in web applications, hence a plethora of test cases are developed to validate the APIs calls. We propose a solution that automates the generation of test cases for REST APIs based on their specifications. In our approach, apart from the automatic generation of test cases, we provide an option for the user to influence the test case generation process. By adding user interaction, we aim to augment the automatic generation of APIs test cases with human testing expertise and specific context. We use the latest version of OpenAPI 3.x and a wide range of coverage metrics to analyze the functionality and performance of the generated test cases, and non-functional metrics to analyze the performance of the APIs. The experiments proved the effectiveness and practicability of our method. [ABSTRACT FROM AUTHOR]

Despite the claimed worth and huge interest regarding the increasing volumes of complex data sets and the rewarding promise to improve research, there is, however, a growing concern regarding data privacy that affects both qualitative and quantitative higher education research. Within the contemporary debates on the impact of Big Data on the nature of higher education research and the effective ways to harmonize Big Data practice with privacy restrictions and regulations, this study sets out to qualitatively examine current issues regarding data privacy, anonymity, informed consent and confidentiality in data-centric higher education research, with a focus on the data collector, data subject and data user. We argue that within current regulations, data protection of research subjects concerns more data collection and disclosure and insufficiently describes use, having procedural implications for both the complex nature of higher education (HE) research and the type of research data being collected. We work our argument through an examination of several factors that call for a reconsideration of data privacy and access to private information in HE research. The conclusions indicate that Big Data-centric HE research is increasingly becoming a mainstream research paradigm which needs to address critical data privacy issues before being widely embraced. [ABSTRACT FROM AUTHOR]