The 7th National Audit Project (NAP7) baseline survey of individual anaesthetists: preparedness for and experiences of peri-operative cardiac arrest

The Royal College of Anaesthetists' 7th National Audit Project baseline survey assessed knowledge, attitudes, practices and experiences of peri-operative cardiac arrests among UK anaesthetists and Anaesthesia Associates. We received 10,746 responses, representing a 71% response rate. In-date training in adult and paediatric advanced life support was reported by 9646 (90%) and 7125 (66%) anaesthetists, respectively. There were 8994 (84%) respondents who were confident in leading a peri-operative cardiac arrest, with males more confident than females, but only 5985 (56%) were confident in leading a debrief and 7340 (68%) communicating with next of kin. In the previous two years, 4806 (46%) respondents had managed at least one peri-operative cardiac arrest, of which 321 (7%) and 189 (4%) of these events involved a child or an obstetric patient, respectively. Respondents estimated the most common causes of peri-operative cardiac arrest to be hypovolaemia, hypoxaemia and cardiac ischaemia, with haemorrhage coming fifth. However, the most common reported causes for the most recently attended peri-operative cardiac arrest were haemorrhage; (927, 20%); anaphylaxis (474, 10%); and cardiac ischaemia (397, 9%). Operating lists or shifts were paused or stopped after 1330 (39%) cardiac arrests and 1693 (38%) respondents attended a debrief, with ‘hot’ debriefs most common. Informal wellbeing support was relatively common (2458, 56%) and formal support was uncommon (472, 11%). An impact on future care delivery was reported by 196 (4%) anaesthetists, most commonly a negative psychological impact. Management of a peri-operative cardiac arrest during their career was reported by 8654 (85%) respondents. The overall impact on professional life was more often judged positive (2630, 30%) than negative (1961, 23%), but impact on personal life was more often negative

Methods of the 7th National Audit Project (NAP7) of the Royal College of Anaesthetists: peri-operative cardiac arrest

Cardiac arrest in the peri-operative period is rare but associated with significant morbidity and mortality. Current reporting systems do not capture many such events, so there is an incomplete understanding of incidence and outcomes. As peri-operative cardiac arrest is rare, many hospitals may only see a small number of cases over long periods, and anaesthetists may not be involved in such cases for years. Therefore, a large-scale prospective cohort is needed to gain a deep understanding of events leading up to cardiac arrest, management of the arrest itself and patient outcomes. Consequently, the Royal College of Anaesthetists chose peri-operative cardiac arrest as the 7th National Audit Project topic. The study was open to all UK hospitals offering anaesthetic services and had a three-part design. First, baseline surveys of all anaesthetic departments and anaesthetists in the UK, examining respondents' prior peri-operative cardiac arrest experience, resuscitation training and local departmental preparedness. Second, an activity survey to record anonymised details of all anaesthetic activity in each site over 4 days, enabling national estimates of annual anaesthetic activity, complexity and complication rates. Third, a case registry of all instances of peri-operative cardiac arrest in the UK, reported confidentially and anonymously, over 1 year starting 16 June 2021, followed by expert review using a structured process to minimise bias. The definition of peri-operative cardiac arrest was the delivery of five or more chest compressions and/or defibrillation in a patient having a procedure under the care of an anaesthetist. The peri-operative period began with the World Health Organization 'sign-in' checklist or first hands-on contact with the patient and ended either 24 h after the patient handover (e.g. to the recovery room or intensive care unit) or at discharge if this occured earlier than 24 h. These components described the epidemiology of peri-operative cardiac arrest in the UK and provide a basis for developing guidelines and interventional studies

The rph1 Gene Is a Common Contributor to the Evolution of Phosphine Resistance in Independent Field Isolates of Rhyzopertha Dominica

Phosphine is the only economically viable fumigant for routine control of insect pests of stored food products, but its continued use is now threatened by the world-wide emergence of high-level resistance in key pest species. Phosphine has a unique mode of action relative to well-characterised contact pesticides. Similarly, the selective pressures that lead to resistance against field sprays differ dramatically from those encountered during fumigation. The consequences of these differences have not been investigated adequately. We determine the genetic basis of phosphine resistance in Rhyzopertha dominica strains collected from New South Wales and South Australia and compare this with resistance in a previously characterised strain from Queensland. The resistance levels range from 225 and 100 times the baseline response of a sensitive reference strain. Moreover, molecular and phenotypic data indicate that high-level resistance was derived independently in each of the three widely separated geographical regions. Despite the independent origins, resistance was due to two interacting genes in each instance. Furthermore, complementation analysis reveals that all three strains contain an incompletely recessive resistance allele of the autosomal rph1 resistance gene. This is particularly noteworthy as a resistance allele at rph1 was previously proposed to be a necessary first step in the evolution of high-level resistance. Despite the capacity of phosphine to disrupt a wide range of enzymes and biological processes, it is remarkable that the initial step in the selection of resistance is so similar in isolated outbreaks

An Estimate of the Numbers and Density of Low-Energy Structures (or Decoys) in the Conformational Landscape of Proteins

The conformational energy landscape of a protein, as calculated by known potential energy functions, has several minima, and one of these corresponds to its native structure. It is however difficult to comprehensively estimate the actual numbers of low energy structures (or decoys), the relationships between them, and how the numbers scale with the size of the protein.We have developed an algorithm to rapidly and efficiently identify the low energy conformers of oligo peptides by using mutually orthogonal Latin squares to sample the potential energy hyper surface. Using this algorithm, and the ECEPP/3 potential function, we have made an exhaustive enumeration of the low-energy structures of peptides of different lengths, and have extrapolated these results to larger polypeptides.We show that the number of native-like structures for a polypeptide is, in general, an exponential function of its sequence length. The density of these structures in conformational space remains more or less constant and all the increase appears to come from an expansion in the volume of the space. These results are consistent with earlier reports that were based on other models and techniques

Pacific island regional preparedness for El Niño

The El Niño Southern Oscillation (ENSO) cycle is often blamed for disasters in Pacific island communities. From a disaster risk reduction (DRR) perspective, the challenges with the El Niño part of the ENSO cycle, in particular, are more related to inadequate vulnerability reduction within development than to ENSO-induced hazard influences. This paper analyses this situation, filling in a conceptual and geographic gap in El Niño-related research, by reviewing El Niño-related preparedness (the conceptual gap) for Pacific islands (the geographic gap). Through exploring El Niño impacts on Pacific island communities alongside their vulnerabilities, resiliences, and preparedness with respect to El Niño, El Niño is seen as a constructed discourse rather than as a damaging phenomenon, leading to suggestions for El Niño preparedness as DRR as part of development. Yet the attention which El Niño garners might bring resources to the Pacific region and its development needs, albeit in the short term while El Niño lasts. Conversely, the attention given to El Niño could shift blame from underlying causes of vulnerability to a hazard-centric viewpoint. Instead of focusing on one hazard-influencing phenomenon, opportunities should be created for the Pacific region to tackle wider DRR and development concerns

CD36 deficiency attenuates experimental mycobacterial infection

<p>Abstract</p> <p>Background</p> <p>Members of the CD36 scavenger receptor family have been implicated as sensors of microbial products that mediate phagocytosis and inflammation in response to a broad range of pathogens. We investigated the role of CD36 in host response to mycobacterial infection.</p> <p>Methods</p> <p>Experimental <it>Mycobacterium bovis </it>Bacillus Calmette-Guérin (BCG) infection in <it>Cd36^+/+</it>and <it>Cd36^-/-</it>mice, and <it>in vitro </it>co-cultivation of <it>M. tuberculosis</it>, BCG and <it>M. marinum </it>with <it>Cd36^+/+</it>and <it>Cd36^-/-</it>murine macrophages.</p> <p>Results</p> <p>Using an <it>in vivo </it>model of BCG infection in <it>Cd36^+/+</it>and <it>Cd36^-/-</it>mice, we found that mycobacterial burden in liver and spleen is reduced (83% lower peak splenic colony forming units, p < 0.001), as well as the density of granulomas, and circulating tumor necrosis factor (TNF) levels in <it>Cd36^-/-</it>animals. Intracellular growth of all three mycobacterial species was reduced in <it>Cd36^-/-</it>relative to wild type <it>Cd36^+/+</it>macrophages <it>in vitro</it>. This difference was not attributable to alterations in mycobacterial uptake, macrophage viability, rate of macrophage apoptosis, production of reactive oxygen and/or nitrogen species, TNF or interleukin-10. Using an <it>in vitro </it>model designed to recapitulate cellular events implicated in mycobacterial infection and dissemination <it>in vivo </it>(i.e., phagocytosis of apoptotic macrophages containing mycobacteria), we demonstrated reduced recovery of viable mycobacteria within <it>Cd36^-/-</it>macrophages.</p> <p>Conclusions</p> <p>Together, these data indicate that CD36 deficiency confers resistance to mycobacterial infection. This observation is best explained by reduced intracellular survival of mycobacteria in the <it>Cd36^-/-</it>macrophage and a role for CD36 in the cellular events involved in granuloma formation that promote early bacterial expansion and dissemination.</p

Multi-state Modeling of Biomolecules

Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the “specification problem”) and the problem of how to use a computer to simulate the progress of the system over time (the “computation problem”). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus [1], BioNetGen [2]–[5], the Allosteric Network Compiler [6], and others [7], [8]. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm [9], [10]. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim [11], DYNSTOC [12], RuleMonkey [9], [13], and the Network-Free Stochastic Simulator (NFSim) [14], and spatial simulators, including Meredys [15], SRSim [16], [17], and MCell [18]–[20]. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future