Safety and efficacy of different anesthetic regimens for parturients with COVID-19 undergoing Cesarean delivery: a case series of 17 patients

Canadian Journal of Anesthesia/Journal canadien d'anesthésie - To assess the management and safety of epidural or general anesthesia for Cesarean delivery in parturients with coronavirus...     

Demonstration of a modelling-based multi-criteria decision analysis procedure for prioritisation of occupational risks from manufactured nanomaterials

Several tools to facilitate the risk assessment and management of manufactured nanomaterials (MN) have been developed. Most of them require input data on physicochemical properties, toxicity and scenario-specific exposure information. However, such data are yet not readily available, and tools that can handle data gaps in a structured way to ensure transparent risk analysis for industrial and regulatory decision making are needed. This paper proposes such a quantitative risk prioritisation tool, based on a multi-criteria decision analysis algorithm, which combines advanced exposure and dose-response modelling to calculate margins of exposure (MoE) for a number of MN in order to rank their occupational risks. We demonstrated the tool in a number of workplace exposure scenarios (ES) involving the production and handling of nanoscale titanium dioxide, zinc oxide (ZnO), silver and multi-walled carbon nanotubes. The results of this application demonstrated that bag/bin filling, manual un/loading and dumping of large amounts of dry powders led to high emissions, which resulted in high risk associated with these ES. The ZnO MN revealed considerable hazard potential in vivo, which significantly influenced the risk prioritisation results. In order to study how variations in the input data affect our results, we performed probabilistic Monte Carlo sensitivity/uncertainty analysis, which demonstrated that the performance of the proposed model is stable against changes in the exposure and hazard input variables.     

Next-generation sequencing through multigene panel testing for the diagnosis of hereditary epidermolysis bullosa in Chinese population

Epidermolysis bullosa (EB) is a heritable blistering disorder. We performed a next-generation sequencing-based multigene panel test and successfully predicted 100% of the EB types, including, 36 EB simplex (EBS), 13 junctional EB (JEB), 86 dystrophic EB (DEB), and 3 Kindler EB. Chinese JEB and recessive DEB (RDEB) patients have relatively mild phenotypes; for severe type separately accounts for 45.5% and 23.8%, respectively. We identified 96 novel and 49 recurrent pathogenic variants in 11 genes, although we failed to detect the second mutation in one JEB and five RDEB patients. We identified one novel p.E475K mosaic mutation in the clinically normal mother of one out of 13 EBS patients with KRT5 mutations, one recurrent p.G2034R mosaic mutation, and one novel p.G2043R mosaic mutation in the clinically normal relatives of two out of 19 dominant DEB patients. This study shows that next-generation technology could be an effective tool in diagnosing EB.