Functional dissection of a eukaryotic transcriptional repressor protein QUTR of Aspergillus nidulans

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN013177 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Model predictive glycaemic regulation in critical illness using insulin and nutrition input: a pilot study

This paper develops and presents a pilot study of a long-term controller for safe regulation of glycaemia under elevated insulin resistance and glucose intolerance in critically ill patients by modulating enteral nutrition inputs in addition to conventional basal-bolus intravenous insulin therapy. Clinical proof-of-concept pilot trials of the algorithm are performed which show the algorithm adaptability to time-varying intraas well as inter-patient variability in condition while requiring relatively infrequent glucose measurement. This research is a step towards randomized, comparative cohort studies of clinical outcomes using the developed protocol. Previous blood glucose control research includes controlled experiments in insulin infusion by Hovorka et al. [26], Chee et al. [27], and Chase et al. [18, 28]. Adaptive bolus-based control using insulin-alone by Chase et al. [18], is the basis of this work. The primary difference in this research is the improvement in control under elevated insulin resistance by modulation of nutritional support in addition to insulin inpu

A novel, model-based insulin and nutrition delivery controller for glycemic regulation in critically ill patients

Background: Critically ill patients are often hyperglycemic and insulin resistant, as well as highly dynamic. Tight glucose control has been shown to significantly reduce mortality in critical care. A physiological model of the glucose–insulin regulatory system is improved and used to develop an adaptive control protocol utilizing both nutritional and insulin inputs to control hyperglycemia. The approach is clinically verified in a critical care patient cohort. Methods: A simple two- compartment model for glucose rate of appearance in plasma due to stepwise enteral glucose fluxes is developed and incorporated into a previously validated system model. A control protocol modulating intravenous insulin infusion and bolus, with an enteral feed rate, is developed, enabling tight and predictive glycemic regulation to preset targets. The control protocol is adaptive to patient time-variant effective insulin resistance. The model and protocol are verified in seven 10-h and one 24-h proof-of-concept clinical trials. Ethics approval was granted by the Canterbury Ethics Committee. Results: Insulin requirements varied widely following acute changes in patient physiology. The algorithm developed successfully adapted to patient metabolic status and insulin sensitivity, achieving an average target acquisition error of 9.3% with 90.7% of all targets achieved within ±20%. Prediction errors may not be distinguishable from sensor measurement errors. Large errors (>20%) are attributable to highly dynamic and unpredictable changes in patient condition. Conclusions: Tight, targeted stepwise regulation was exhibited in all trials. Overall, tight glycemic regulation is achieved in a broad critical care cohort with optimized insulin and nutrition delivery, effectively managing glycemia even with high effective insulin resistance

Genomic epidemiology of SARS-CoV-2 in a university outbreak setting and implications for public health planning

Whole genome sequencing of SARS-CoV-2 has occurred at an unprecedented scale, and can be exploited for characterising outbreak risks at the fine-scale needed to inform control strategies. One setting at continued risk of COVID-19 outbreaks are higher education institutions, associated with student movements at the start of term, close living conditions within residential halls, and high social contact rates. Here we analysed SARS-CoV-2 whole genome sequences in combination with epidemiological data to investigate a large cluster of student cases associated with University of Glasgow accommodation in autumn 2020, Scotland. We identified 519 student cases of SARS-CoV-2 infection associated with this large cluster through contact tracing data, with 30% sequencing coverage for further analysis. We estimated at least 11 independent introductions of SARS-CoV-2 into the student population, with four comprising the majority of detected cases and consistent with separate outbreaks. These four outbreaks were curtailed within a week following implementation of control measures. The impact of student infections on the local community was short-term despite an underlying increase in community infections. Our study highlights the need for context-specific information in the formation of public health policy for higher educational settings