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Digital literacy competence (DL) is an important capacity for students’ learning in a rapidly changing world. However, little is known about the empirical structure of DL. In this paper, we review major DL assessment frameworks and explore the dimensionality of DL from an empirical perspective using assessment data collected using authentic software applications, rather than simulated assessment environments. Secondary analysis on representative data collected from primary and secondary school students in Hong Kong using unidimensional and multidimensional item response theory reveals a general dimension of digital literacy performance and four specific, tool-dependent dimensions. These specific DL dimensions are defined by the software applications that students use and capture commonality among students’ performance that is due to their familiarity with the assessment tools and contexts. The design of DL assessment is discussed in light of these findings, with particular emphasis on the influence of the nature of digital applications and environments used in assessment on the DL achievement scores measured.

Pre-exposure prophylaxis for COVID-19 with tixagevimab/cilgavimab (T/C) received Emergency Use Authorization (EUA) based on results of a clinical trial conducted prior to the emergence of the Omicron variant. The clinical effectiveness of T/C has not been well described in the Omicron era. We examined the incidence of symptomatic illness and hospitalizations among T/C recipients when Omicron accounted for virtually all local cases. Through retrospective electronic medical record chart review, we identified patients who received T/C between January 1 -July 31, 2022 within our quaternary referral health system. We determined the incidence of symptomatic COVID-19 infections and hospitalizations due to or presumed to be caused by early Omicron variants before and after receiving T/C (pre-T/C and post-T/C). Chi square and Mann-Whitney Wilcoxon two-sample tests were used to examine differences between the characteristics of those who got COVID-19 before or after T/C prophylaxis, and rate ratios (RR) and 95% confidence intervals (CI) were calculated to assess differences in hospitalization rates for the two groups. Of 1295 T/C recipients, 105 (8.1%) developed symptomatic COVID-19 infection before receiving T/C, and 102 (7.9%) developed symptomatic disease after receiving it. Of the 105 patients who developed symptomatic infection pre-T/C, 26 (24.8%) were hospitalized, compared with six of the 102 patients (5.9%) who were diagnosed with COVID-19 post-T/C (RR = 0.24; 95% CI = 0.10-0.55; p = 0.0002). Seven of the 105 (6.7%) patients infected pre-T/C, but none of the 102 infected post-T/C required ICU care. No COVID-related deaths occurred in either group. The majority of COVID-19 cases among those infected pre-T/C treatment occurred during the Omicron BA.1 surge, while the majority of post-T/C cases occurred when Omicron BA.5 was predominant. In both groups, having at least one dose of vaccine strongly protected against hospitalization (pre-T/C group RR = 0.31, 95% CI = 0.17-0.57, p = 0.02; post-T/C group RR = 0.15; 95% CI = 0.03-0.94; p = 0.04). We identified COVID-19 infections after T/C prophylaxis. Among patients who received T/C at our institution, COVID-19 Omicron cases occurring after T/C were one-fourth as likely to require hospitalization compared to those with Omicron prior to T/C. However, due to the presence of changing vaccine coverage, multiple therapies, and changing variants, the effectiveness of T/C in the Omicron era remains difficult to assess.

The acquisition or reactivation of Epstein–Barr virus (EBV) after allogeneic Hematopoietic Stem Cell Transplant (HSCT) can be associated with complications including the development of post-transplant lymphoproliferative disorder (PTLD), which is associated with significant morbidity and mortality. A number of risk factors for PTLD have been defined, including T-cell depletion, and approaches to monitoring EBV, especially in high-risk patients, with the use of preemptive therapy upon viral activation have been described. Newer therapies for the preemption or treatment of PTLD, such as EBV-specific cytotoxic T-cells, hold promise. Further studies to help define risks, diagnosis, and treatment of EBV-related complications are needed in this at-risk population.

IntroductionWe describe the use of bacteriophage therapy in a 26-year-old cystic fibrosis (CF) patient awaiting lung transplantation.Hospital CourseThe patient developed multidrug resistant (MDR) Pseudomonas aeruginosa pneumonia, persistent respiratory failure, and colistin-induced renal failure. We describe the use of intravenous bacteriophage therapy (BT) along with systemic antibiotics in this patient, lack of adverse events, and clinical resolution of infection with this approach. She did not have recurrence of pseudomonal pneumonia and CF exacerbation within 100 days following the end of BT and underwent successful bilateral lung transplantation 9 months later.ConclusionGiven the concern for MDR P. aeruginosa infections in CF patients, BT may offer a viable anti-infective adjunct to traditional antibiotic therapy.

In this study, we review 1830 research articles on artificial intelligence in education (AIED), with the aim of providing a holistic picture of the knowledge evolution in this interdisciplinary research field from 2010 to 2019. A novel three-step approach in the analysis of the keyword co-occurrence networks (KCN) is proposed to identify the knowledge structure, knowledge clusters and trending keywords within AIED over time. The results reveal considerable research diversity in the AIED field, centering around two sustained themes: intelligent tutoring systems (2010-19) and massive open online courses (since 2014). The focal educational concerns reflected in AIED research are: (1) online learning; (2) game-based learning; (3) collaborative learning; (4) assessment; (5) affect; (6) engagement; and (7) learning design. The highly connected keywords relevant to analytic techniques within this field include natural language processing, educational data mining, learning analytics and machine learning. Neural network, deep learning, eye tracking, and personalized learning are trending keywords in this field as they have emerged with key structural roles in the latest two-year period analyzed. This is the first article providing a systematic review of a large body of literature on artificial intelligence in education, and in it we uncover the underlying patterns of knowledge connectivity within the field, as well as provide insight into its future development. The three-step multi-scale (macro, meso, micro) framework proposed in this study can also be applied to map the knowledge development in other scientific research areas.

In this paper we elaborate and extend the work of the EDUsummIT 2015 Thematic Working Group 7 (TWG7) by proposing a set of indicators on quality Technology-Enhanced Learning and Teaching (TEL＆T). These indicators are intended as one component of a set of global indicators that could be used to monitor implementation of the Education 2030 agenda, which aims to “Ensure inclusive and equitable quality education and promote life-long learning opportunities for all.” The proposed indicators address conditions at the student, teacher, school and system levels, and are organized in a systemic framework to help foreground interactions and interdependencies within and across the different levels. This framework highlights the need for longitudinal, multilevel designs in evaluation studies of TEL＆T implementations, which will also contribute to a better understanding of the links between policy, policy implementation, outputs, and outcomes. Sample indicators are presented to illustrate the framework, and suggestions are made for use of the framework in evaluation studies. Further, the proposed framework could be used to underpin the development of an open, worldwide collaborative of educational evaluation researchers, practitioners and policymakers, thereby adopting a crowdsourcing approach to systematically address the complex challenges in evaluating quality TEL＆T.

Background Acinetobacter baumannii is a nosocomial pathogen which is establishing as a major cause of morbidity and mortality within the healthcare community. The success of this pathogen is largely due to its ability to rapidly gain resistance to antimicrobial therapies and its capability to persist in an abiotic environment through the production of a biofilm. Our tertiary-care hospital has showed high incidence of carbapenem-resistant Acinetobacter baumannii (CRAB) isolates. Methods In this study we explore both genotypic and phenotypic properties of 26 CRAB isolates: 16 isolates were collected from January 2010 to March 2011, and 10 were collected between February and May 2015. Results We determined that all 26 CRAB isolates possessed multiple β-lactamase genes, including genes from Groups A, C, and D. Specifically, 42% of the isolates possesses the potentially plasmid-borne genes of OXA-23-like or OXA-40-like β-lactamase. The presence of mobile gene element integron cassettes and/or integrases in 88% of the isolates suggests a possible mechanism of dissemination of antibiotic resistance genes. Additionally, the location of insertion sequence (IS) IS Aba 1 in promotor region of of the OXA-51-like, ADC-7, and ampC genes was confirmed. Multilocus sequence typing (MLST) demonstrated that all 26 CRAB isolates were either sequence type (ST)-229 or ST-2. Interestingly, ST-2 went from being the minority CRAB strain in the 2010–2011 isolates to the predominant strain in the 2015 isolates (from 32 to 90%). We show that the ST-2 strains have an enhanced ability to produce biofilms in comparison to the ST-229 strains, and this fact has potentially led to more successful colonization of the clinical environment over time. Conclusions This study provides a longitudinal genetic and phenotypic survey of two CRAB sequence types, and suggests how their differing phenotypes may interact with the selective pressures of a hospital setting effecting strain dominance over a 5-year period.

IntroductionMyasthenia gravis (MG) is a rare, chronic, autoimmune disease, mediated by immunoglobulin G antibodies, which causes debilitating muscle weakness. As with most rare diseases, there is little patient-reported data with which to understand and address patient needs. This study explores the impact of MG in the real world from the patient perspective.Methods and analysisThis is a 2-year prospective, observational, digital, longitudinal study of adults with MG, resident in the following countries: the USA, Japan, Germany, France, the UK, Italy, Spain, Canada and Belgium. The planned sample size is 2000. Recruitment will be community based, via patient advocacy groups, social media and word of mouth. Participants will use a smartphone application (app) to check eligibility, provide consent and contribute data. Planned data entry is as follows: (1) personal profile on enrollment—covering demographics, MG characteristics and previous care; (2) monthly event tracker—current treatments, healthcare visits, treatment-related adverse events, productivity losses; (3) monthly selection of validated generic and disease-specific patient-reported outcomes instruments: EQ-5D-5L, Myasthenia Gravis Activities of Daily Living, Myasthenia Gravis Quality of Life 15-item revised scale, Hospital Anxiety and Depression Scale and Health Utilities Index III. Analyses are planned for when the study has been running in most countries for approximately 6, 12, 18 and 24 months.Ethics and disseminationThe study protocol has been reviewed and granted ethics approval by Salus IRB for participants resident in the following countries: Germany, the UK and the US. Local ethics approval is being sought for the following study countries: Belgium, Canada, France, Italy, Japan and Spain. Study results will be communicated to the public and participants via conference presentations and journal publications, as well as regular email, social media and in-application communication.Trial registration numberNCT04176211.

Cycling is associated with a greater risk of traumatic brain injury (TBI) than other recreational activities. This study aimed to investigate the epidemiology of sports-related TBI in Hong Kong and to examine predictors for recreational cycling-induced intracranial haemorrhage. This retrospective multicentre study included patients diagnosed with sports-related TBI in public hospitals in Hong Kong from 2015 to 2019. Computed tomography scans were reviewed by an independent assessor. The primary endpoint was traumatic intracranial haemorrhage. The secondary endpoint was an unfavourable Glasgow Outcome Scale (GOS) score at discharge from hospital. In total, 720 patients were hospitalised with sports-related TBI. The most common sport was cycling (59.2%). The crude incidence of cycling-related TBI was 1.1 per 100 000 population. Cyclists were more likely to exhibit intracranial haemorrhage and an unfavourable GOS score, compared with patients who had TBI because of other sports. Although 47% of cyclists had intracranial haemorrhage, only 15% wore a helmet. In multivariate analysis, significant predictors for intracranial haemorrhage were age ≥60 years, antiplatelet medication, moderate or severe TBI, and skull fracture. Among 426 cyclists, 375 (88%) had mild TBI, and helmet wearing was protective against intracranial haemorrhage, regardless of age, antiplatelet medication intake, and mechanism of injury. Of 426 cyclists, 31 (7.3%) had unfavourable outcomes on discharge from hospital. The incidence of sports-related TBI is low in Hong Kong. Although cycling-related head injuries carried greater risks of intracranial haemorrhage and unfavourable outcomes compared with other sports, most cyclists experienced good recovery. Helmet wearing among recreational cyclists with mild TBI was protective against intracranial haemorrhage and skull fracture.