Shock: aetiology, pathophysiology and management.

The term 'shock' is used to describe a complex, life-threatening clinical condition that arises from acute circulatory failure. Shock is a pathological state that results when the circulation is unable to deliver sufficient oxygen and nutrients to the cells and tissues. The resulting hypoxia, tissue hypoperfusion and cellular dysfunction can lead to multi-organ failure; if this is not treated in a timely and appropriate manner, it can lead to death. This article gives an introduction to shock with an overview of the condition and its physiological impact on patients. Focusing on the aetiology and underlying causes, discussion will highlight the different types, stages and general pathophysiology of shock, as well as providing a guide to treatment options and nursing interventions.

Predicting the spatio-temporal infection risk in indoor spaces using an efficient airborne transmission model.

We develop a spatially dependent generalization to the Wells-Riley model, which determines the infection risk due to airborne transmission of viruses. We assume that the infectious aerosol concentration is governed by an advection-diffusion-reaction equation with the aerosols advected by airflow, diffused due to turbulence, emitted by infected people, and removed due to ventilation, inactivation of the virus and gravitational settling. We consider one asymptomatic or presymptomatic infectious person breathing or talking, with or without a mask, and model a quasi-three-dimensional set-up that incorporates a recirculating air-conditioning flow. We derive a semi-analytic solution that enables fast simulations and compare our predictions to three real-life case studies-a courtroom, a restaurant, and a hospital ward-demonstrating good agreement. We then generate predictions for the concentration and the infection risk in a classroom, for four different ventilation settings. We quantify the significant reduction in the concentration and the infection risk as ventilation improves, and derive appropriate power laws. The model can be easily updated for different parameter values and can be used to make predictions on the expected time taken to become infected, for any location, emission rate, and ventilation level. The results have direct applicability in mitigating the spread of the COVID-19 pandemic.