ABSTRACT
Leuconostoc lactis (LLac) is a Gram-positive coccus of the family Leuconostocaceae . It can be found in a variety of vegetables and dairy products. LLac is an opportunistic pathogen with intrinsic resistance to vancomycin and teicoplanin. In this case report, we discuss a rare case of LLac-associated bacteraemia in a patient with osteopetrosis. A 4-year-old girl was admitted to the paediatric emergency department with acute fever without other signs. Blood culture revealed an infection with LLac. Using the streptococcus antibiogram, the isolate was resistant to vancomycin, teicoplanin, rifampicin and sulfamethoxazole-trimethoprim but sensitive to ß-lactams, gentamicin, streptomycin, azithromycin, clarithromycin, lincomycin, clindamycin and erythromycin. The patient was treated with intravenous ceftriaxone and gentamicin, and subsequently with oral amoxicillin. After a favourable course, she was discharged from the hospital on the 10th day. The modes of transmission and physiopathology of LLac remain unknown. Factors associated with this infection include compromised immunity, previous antibiotic therapy especially with vancomycin, and application of a central venous catheter. In our patient, the risk factors for infection were pancytopenia and multiple transfusions used to treat bone marrow failure. The source of the bacteraemia could have been the cutaneous route, but it could also have been digestive due to the reservoir of the bacteria. LLac is known as an opportunistic bacterium. Further studies on its pathogenesis and other risk factors are needed to understand the true prevalence of this potentially fatal bacterium in compromised individuals, such as the case of our patient.
ABSTRACT
INTRODUCTION: The pre-analytical step of cytobacteriological examination of urine (CBEU) is one of the most critical in microbiology. AIM: To analyze quantitatively and qualitatively the pre-analytical non-conformities related to the CBEU in order to propose reliable corrective measures. METHOD: This was a 76-month retrospective study from March 2016 to June 2022. The study included all CBEU referred to our laboratory. The conformity of the requests was evaluated according to the requirements of the medical microbiology standard (REMIC). It concerned the CBEU request, the urine sample and its packaging. RESULT: We collected 66631 CBEU requests. The urine was not conform in 1646 (2.47%) cases. The majority of non-conformities came from the emergency department (n= 653; 39.67%). The predominant non-conformities were (i) deteriorated sample (53.53%; n=878), (ii) delayed transport (28.55%; n=469) and (iii) damaged equipment (4.62%; n= 76). CONCLUSION: In our study, pre-analytical non-conformities of CBEU were frequent and affected all steps of the pre-analytical process. They had a direct clinical and economic impact on the patient. Continuous improvement of the pre-analytical phase of the CBEU is necessary in our institution.
Subject(s)
Dioctyl Sulfosuccinic Acid , Urinalysis , Humans , Retrospective Studies , Emergency Service, Hospital , Hospitals, UniversityABSTRACT
The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice in Learning Analytics and Educational Data Mining. The aim of this study is to review the most recent research body related to Predictive Analytics in Higher Education. Articles published during the last decade between 2012 and 2022 were systematically reviewed following PRISMA guidelines. We identified the outcomes frequently predicted in the literature as well as the learning features employed in the prediction and investigated their relationship. We also deeply analyzed the process of predictive modelling, including data collection sources and types, data preprocessing methods, Machine Learning models and their categorization, and key performance metrics. Lastly, we discussed the relevant gaps in the current literature and the future research directions in this area. This study is expected to serve as a comprehensive and up-to-date reference for interested researchers intended to quickly grasp the current progress in the Predictive Learning Analytics field. The review results can also inform educational stakeholders and decision-makers about future prospects and potential opportunities.
ABSTRACT
Artificial Intelligence (AI) has played a substantial role in the response to the challenges posed by the current pandemic. The growing interest in using AI to handle Covid-19 issues has accelerated the pace of AI research and resulted in an exponential increase in articles and review studies within a very short period of time. Hence, it is becoming challenging to explore the large corpus of academic publications dedicated to the global health crisis. Even with the presence of systematic review studies, given their number and diversity, identifying trends and research avenues beyond the pandemic should be an arduous task. We conclude therefore that after the one-year mark of the declaration of Covid-19 as a pandemic, the accumulated scientific contribution lacks two fundamental aspects: Knowledge synthesis and Future projections. In contribution to fill this void, this paper is a (i) synthesis study and (ii) foresight exercise. The synthesis study aims to provide the scholars a consolidation of findings and a knowledge synthesis through a systematic review of the reviews (umbrella review) studying AI applications against Covid-19. Following the PRISMA guidelines, we systematically searched PubMed, Scopus, and other preprint sources from 1st December 2019 to 1st June 2021 for eligible reviews. The literature search and screening process resulted in 45 included reviews. Our findings reveal patterns, relationships, and trends in the AI research community response to the pandemic. We found that in the space of few months, the research objectives of the literature have developed rapidly from identifying potential AI applications to evaluating current uses of intelligent systems. Only few reviews have adopted the meta-analysis as a study design. Moreover, a clear dominance of the medical theme and the DNN methods has been observed in the reported AI applications. Based on its constructive systematic umbrella review, this work conducts a foresight exercise that tries to envision the post-Covid-19 research landscape of the AI field. We see seven key themes of research that may be an outcome of the present crisis and which advocate a more sustainable and responsible form of intelligent systems. We set accordingly a post-pandemic research agenda articulated around these seven drivers. The results of this study can be useful for the AI research community to obtain a holistic view of the current literature and to help prioritize research needs as we are heading toward the new normal.
