Non-fatal opioid overdose, naloxone access, and naloxone training among people who recently used opioids or received opioid agonist treatment in Australia: The ETHOS Engage study

BACKGROUND

Overdose is a major cause of morbidity and mortality among people who use opioids. Naloxone can reverse opioid overdoses and can be distributed and administered with minimal training. People with experience of overdose are a key population to target for overdose prevention strategies. This study aims to understand if factors associated with recent non-fatal opioid overdose are the same as factors associated with naloxone access and naloxone training in people who recently used opioids or received opioid agonist treatment (OAT).

METHODS

ETHOS Engage is an observational study of people who inject drugs in Australia. Logistic regression models were used to estimate odds ratios for non-fatal opioid overdose, naloxone access and naloxone training.

RESULTS

Between May 2018-September 2019, 1280 participants who recently used opioids or received OAT were enrolled (62% aged >40 years; 35% female, 80% receiving OAT, 62% injected drugs in the preceding month). Recent opioid overdose (preceding 12 months) was reported by 7% of participants, lifetime naloxone access by 17%, and lifetime naloxone training by 14%. Compared to people receiving OAT with no additional opioid use, recent opioid, benzodiazepine (preceding six months), and hazardous alcohol use was associated with recent opioid overdose (aOR 3.91; 95%CI: 1.68-9.10) and lifetime naloxone access (aOR 2.12; 95%CI 1.29-3.48). Among 91 people who reported recent overdose, 65% had never received take-home naloxone or naloxone training.

CONCLUSIONS

Among people recently using opioids or receiving OAT, benzodiazepine and hazardous alcohol use is associated with non-fatal opioid overdose. Not all factors associated with non-fatal overdose correspond to factors associated with naloxone access. Naloxone access and training is low across all groups. Additional interventions are needed to scale up naloxone provision.

Model selection in systems biology depends on experimental design

Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis.

Different models of the same process represent distinct hypotheses about reality. These can be decided between within the framework of model selection, where the evidence for each is given by their ability to reproduce a set of experimental data. Even if one of the models is correct, the chances of identifying it can be hindered by the quality of the data, both in terms of its signal to measurement error ratio and the intrinsic discriminatory potential of the experiment undertaken. This potential can be predicted in various ways, and maximising it is one aim of experimental design. In this work we present a computationally efficient method of experimental design for model selection. We exploit the efficiency to consider the implications of the realistic case where all models are more or less incorrect, showing that experiments can be chosen that, considered individually, lead to unequivocal support for opposed hypotheses.

Combinatorial stresses kill pathogenic Candida species

Pathogenic microbes exist in dynamic niches and have evolved robust adaptive responses to promote survival in their hosts. The major fungal pathogens of humans, Candida albicans and Candida glabrata, are exposed to a range of environmental stresses in their hosts including osmotic, oxidative and nitrosative stresses. Significant efforts have been devoted to the characterization of the adaptive responses to each of these stresses. In the wild, cells are frequently exposed simultaneously to combinations of these stresses and yet the effects of such combinatorial stresses have not been explored. We have developed a common experimental platform to facilitate the comparison of combinatorial stress responses in C. glabrata and C. albicans. This platform is based on the growth of cells in buffered rich medium at 30°C, and was used to define relatively low, medium and high doses of osmotic (NaCl), oxidative (H ₂O₂) and nitrosative stresses (e.g., dipropylenetriamine (DPTA)-NONOate). The effects of combinatorial stresses were compared with the corresponding individual stresses under these growth conditions. We show for the first time that certain combinations of combinatorial stress are especially potent in terms of their ability to kill C. albicans and C. glabrata and/or inhibit their growth. This was the case for combinations of osmotic plus oxidative stress and for oxidative plus nitrosative stress. We predict that combinatorial stresses may be highly signif cant in host defences against these pathogenic yeasts.

Bayesian design strategies for synthetic biology

We discuss how statistical inference techniques can be applied in the context of designing novel biological systems. Bayesian techniques have found widespread application and acceptance in the systems biology community, where they are used for both parameter estimation and model selection. Here we show that the same approaches can also be used in order to engineer synthetic biological systems by inferring the structure and parameters that are most likely to give rise to the dynamics that we require a system to exhibit. Problems that are shared between applications in systems and synthetic biology include the vast potential spaces that need to be searched for suitable models and model parameters; the complex forms of likelihood functions; and the interplay between noise at the molecular level and nonlinearity in the dynamics owing to often complex feedback structures. In order to meet these challenges, we have to develop suitable inferential tools and here, in particular, we illustrate the use of approximate Bayesian computation and unscented Kalman filtering-based approaches. These partly complementary methods allow us to tackle a number of recurring problems in the design of biological systems. After a brief exposition of these two methodologies, we focus on their application to oscillatory systems.

Designing attractive models via automated identification of chaotic and oscillatory dynamical regimes

Chaos and oscillations continue to capture the interest of both the scientific and public domains. Yet despite the importance of these qualitative features, most attempts at constructing mathematical models of such phenomena have taken an indirect, quantitative approach, for example, by fitting models to a finite number of data points. Here we develop a qualitative inference framework that allows us to both reverse-engineer and design systems exhibiting these and other dynamical behaviours by directly specifying the desired characteristics of the underlying dynamical attractor. This change in perspective from quantitative to qualitative dynamics, provides fundamental and new insights into the properties of dynamical systems.

Bayesian design of synthetic biological systems

Here we introduce a new design framework for synthetic biology that exploits the advantages of Bayesian model selection. We will argue that the difference between inference and design is that in the former we try to reconstruct the system that has given rise to the data that we observe, whereas in the latter, we seek to construct the system that produces the data that we would like to observe, i.e., the desired behavior. Our approach allows us to exploit methods from Bayesian statistics, including efficient exploration of models spaces and high-dimensional parameter spaces, and the ability to rank models with respect to their ability to generate certain types of data. Bayesian model selection furthermore automatically strikes a balance between complexity and (predictive or explanatory) performance of mathematical models. To deal with the complexities of molecular systems we employ an approximate Bayesian computation scheme which only requires us to simulate from different competing models to arrive at rational criteria for choosing between them. We illustrate the advantages resulting from combining the design and modeling (or in silico prototyping) stages currently seen as separate in synthetic biology by reference to deterministic and stochastic model systems exhibiting adaptive and switch-like behavior, as well as bacterial two-component signaling systems.

Randomized clinical trial of the effects of preoperative and postoperative oral nutritional supplements on clinical course and cost of care

BACKGROUND

Postoperative oral nutritional supplementation has been shown to be of clinical benefit. This study examined the clinical effects and cost of administration of oral supplements both before and after surgery.

METHODS

This was a randomized clinical trial conducted in three centres. Patients undergoing lower gastrointestinal tract surgery were randomized to one of four groups: group CC received no nutritional supplements, group SS took supplements both before and after surgery, group CS received postoperative supplements only, and group SC were given supplements only before surgery. Preoperative supplements were given from the time it was decided to operate to 1 day before surgery. Postoperative supplements were started when the patient was able to take free fluids and continued for 4 weeks after discharge from hospital. Data collected included weight change, complications, length of stay, nutritional intake, anthropometrics, quality of life and detailed costings covering all aspects of care.

RESULTS

Some 179 patients were randomized, of whom 27 were withdrawn and 152 analysed (CC 44, SS 32, CS 35, SC 41). Dietary intake was similar in all four groups throughout the study. Mean energy intake from preoperative supplements was 536 and 542 kcal/day in the SS and SC groups respectively; that 2 weeks after discharge from hospital was 274 and 361 kcal/day in the SS and CS groups respectively. There was significantly less postoperative weight loss in the SS group than in the CC and CS groups (P < 0.050), and significantly fewer minor complications in the SS and CS groups than the CC group (P < 0.050). There were no differences in the rate of major complications, anthropometrics and quality of life. Mean overall costs were greatest in the CC group, although differences between groups were not significant.

CONCLUSION

Perioperative oral nutritional supplementation started before hospital admission for lower gastrointestinal tract surgery significantly diminished the degree of weight loss and incidence of minor complications, and was cost-effective.

Nutritional support in hospitals in the United Kingdom: national survey 1988

The increasing sophistication of enteral and total parenteral nutritional support techniques has resulted in improvements in the clinical practice of such support in recent years. This survey was designed to establish current clinical practice in the management of nutritionally-compromised hospital patients in 206 districts in the United Kingdom. However, despite recent developments in these techniques, the response revealed a wide variation in the practice of clinical nutritional support. The results also indicate that in each district there should be a group of people, with an interest in clinical nutrition, to monitor and advise on nutritional support. It is suggested that, a national multidisciplinary group should be formed, similar to the American and European Societies of Parenteral and Enteral Nutrition. The group would promote the appropriate use of, and research into, nutritional support specifically for the nutritionally compromised patient in the United Kingdom.

Enteral nutrition: background, indications and management

Enteral nutrition is only part of the wider field of clinical nutrition in which great advances in both theory and practice have been made over the last decade. We have attempted to summarize what we consider to be the advances that have most relevance to the clinical practice of enteral nutrition. This chapter reviews our present understanding of the processes of digestion and absorption of protein, carbohydrate and fats, and examines how this theoretical understanding can be applied to patients in the clinical situation. A broad classification of the different enteral diets is undertaken, and the reasons for the development of particular diets are discussed. The clinical value of these diets is assessed. The wide variety of indications for enteral (as opposed to parenteral) nutrition are discussed and the specific benefits of enteral nutrition for the patient are highlighted. Techniques of administration of enteral nutrition are reviewed in detail, and the methods by which enteral nutrition should be monitored are outlined. Finally, complications of enteral nutrition are summarized and advice given on how to prevent or treat them.