To determine the existence of concentration–toxicity relationships for common β-lactam antibiotic adverse effects and define thresholds above which toxicity is more likely. Retrospective review of consecutive patients treated with piperacillin, meropenem or flucloxacillin who underwent therapeutic drug monitoring (TDM) at St Vincent’s Hospital (Sydney, Australia) between January 2013 and December 2015. Adverse events investigated included neurotoxicity, nephrotoxicity, hepatotoxicity and opportunistic Clostridium difficile infection. Toxicity was measured using observational grading criteria, clinical assessment and relevant serum biomarkers. These findings were correlated with trough TDM measurements at the time of toxicity presentation. TDM results from 378 patients (piperacillin = 223, meropenem = 94 and flucloxacillin = 61) were investigated. There was no difference in baseline patient characteristics across antibiotic groups. A statistically significant elevation in mean serum trough concentrations (C_min) was found in patients diagnosed with neurotoxicity (piperacillin, P < 0.01; meropenem, P = 0.04; flucloxacillin, P = 0.01) and those who developed nephrotoxicity whilst being treated with piperacillin (P < 0.01) or meropenem (P < 0.01). Incidence of hepatotoxicity and C. difficile was not related to C_min. Threshold concentrations for which there is 50% risk of developing a neurotoxicity event (piperacillin, C_min >361.4 mg/L; meropenem, C_min >64.2 mg/L; flucloxacillin, C_min >125.1 mg/L) or nephrotoxicity (piperacillin, C_min >452.65 mg/L; meropenem, C_min >44.45 mg/L) varied across antibiotics. Our data reveal an association between toxic concentrations for a number of β-lactam agents and neurotoxic/nephrotoxic effects. We have defined threshold concentrations above which these toxicities become more likely. Clinicians should balance concerns for therapeutic efficacy with potential toxicity when considering aggressive therapy.

A high intake of refined carbohydrates, particularly the monosaccharide fructose, has been attributed to the growing epidemics of obesity and type-2 diabetes. Animal studies have helped elucidate the metabolic effects of dietary fructose, however, variations in study design make it difficult to draw conclusions. The aim of this study was to review the effects of fructose beverage consumption on body weight, systolic blood pressure and blood glucose, insulin and triglyceride concentrations in validated rat models. We searched Ovid Embase Classic + EmbaseMedline and Ovid Medline databases and included studies that used adolescent/adult male rats, with fructose beverage consumption for >3 weeks. Data from 26 studies were pooled by an inverse variance weighting method using random effects models, expressed as standardized mean differences (SMD) with 95% confidence intervals (CI). Overall, 10%–21% w/v fructose beverage consumption was associated with increased rodent body weight (SMD, 0.62 (95% CI: 0.18, 1.06)), systolic blood pressure (SMD, 2.94 (95% CI: 2.10, 3.77)) and blood glucose (SMD, 0.77 (95% CI: 0.36, 1.19)), insulin (SMD, 2.32 (95% CI: 1.57, 3.07)) and triglyceride (SMD, 1.87 (95% CI: 1.39, 2.34)) concentrations. Therefore, the consumption of a low concentration fructose beverage is sufficient to cause early signs of the metabolic syndrome in adult rats.

Research and development in learning analytics has established viable solutions for scaling personalised feedback to all students. However, questions remain regarding how such feedback is perceived, interpreted and acted upon by stakeholders. The present study reports on the analysis of focus group data from four courses to understand students’ sensemaking of personalised, learning analytics-based feedback. Deductive analysis and epistemic network analysis were carried out to identify students’ perceptions and associated emotional responses to the feedback. The results demonstrate that students mostly held positive perceptions of their feedback – especially in regard to quality, task-focus and instructor care. While emotions to feedback were often negative, students also noted this increased their motivation to invest more effort in the learning tasks. The results of the study provide further evidence of the interpersonal communication variables influencing students’ proactive recipience of feedback. Importantly, the findings show that students were mostly actively engaged with their personalised feedback, and that the instructor-mediated approach to feedback based on learning analytics is aligned to the principles of effective feedback.