Neuregulin 1 acts as an axonal signal that regulates multiple aspects of Schwann cell development including the survival and migration of Schwann cell precursors, the ensheathment of axons and subsequent elaboration of the myelin sheath. To examine the role of this factor in remyelination and repair following nerve injury, we ablated neuregulin 1 in the adult nervous system using a tamoxifen inducible Cre recombinase transgenic mouse system. The loss of neuregulin 1 impaired remyelination after nerve crush, but did not affect Schwann cell proliferation associated with Wallerian degeneration or axon regeneration or the clearance of myelin debris by macrophages. Myelination changes were most marked at 10 days after injury but still apparent at 2 months post-crush. Transcriptional analysis demonstrated reduced expression of myelin-related genes during nerve repair in animals lacking neuregulin 1. We also studied repair over a prolonged time course in a more severe injury model, sciatic nerve transection and reanastamosis. In the neuregulin 1 mutant mice, remyelination was again impaired 2 months after nerve transection and reanastamosis. However, by 3 months post-injury axons lacking neuregulin 1 were effectively remyelinated and virtually indistinguishable from control. Neuregulin 1 signalling is therefore an important factor in nerve repair regulating the rate of remyelination and functional recovery at early phases following injury. In contrast to development, however, the determination of myelination fate following nerve injury is not dependent on axonal neuregulin 1 expression. In the early phase following injury, axonal neuregulin 1 therefore promotes nerve repair, but at late stages other signalling pathways appear to compensate.

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).

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In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.

Simulation-based clinical systems testing (SbCST) is a process that allows clinicians and hospital stakeholders to evaluate work carried out in new environments. Unlike work-as-imagined, SbCST takes into account the complex interactions resulting from human performance limitations [1]. These factors can result in errors that may even lead to patient harm [2]. Therefore, we used SbCST to evaluate a newly built children’s emergency department with the aim of identifying latent errors and implementing changes to minimise the risk of their occurrence, whilst also ensuring that the simulation experience was an independently valuable educational opportunity. Scenarios were created according to two criteria. Firstly, that they tested at least one specific environmental issue and secondly, that they focused on topics that the paediatric and Accident and Emergency departments felt would be educationally valuable to the participants. Once created, these scenarios were then carried out as un-announced in-situ simulations during the first 8-weeks of departmental opening. The participants were instructed to treat the scenarios as real, including the manner in which they called for help. Any equipment required came from the department and if single use, it was exchanged for training equipment. The participants then undertook a hot debriefing before feedback was gathered about both the educational value of the scenarios as well as any issues identified within the new department. In total there were 38 multidisciplinary participants including nurses, operating department practitioners, and doctors from 6 different specialties. The feedback from the sessions was positive with an average ranking of >4 out of 5 in 8 out of the 9 measured domains, including; realism, enhancement of knowledge, and usefulness of in-situ simulation in a new environment. We also identified greater than 50 problems spanning all 5 of the categories from the ‘SHEEP’ model [3]. Approximately 60% of issues were resolved within the 8 weeks, whilst the remaining are on the risk register and awaiting review at a stakeholder level. In-situ simulation is an excellent mechanism for carrying out clinical systems testing of new environments due to the fact that it simulates realistic events which are prone to the same errors as the real events, without the risk of patient harm. Once the source of an error is exposed the debriefing can help to identify methods to minimise the risk of future reoccurrences. At the same time, with appropriate planning, the scenarios can also provide an opportunity to deliver multidisciplinary training. 1. Colman N, Doughty C, Arnold J, Stone K, Reid J, Dalpiaz A, Hebbar KB. Simulation-based clinical systems testing for healthcare spaces: from intake through implementation. Advances in Simulation. 2019;4(1):1–9. 2. Reason J. Human error: models and management. Br Med J. 2000;320:768–770. 3. Rosenorn-Lanng D. Human factors in healthcare: level one. Oxford: Oxford university press. 2014.