The use of the Airtraq® optical laryngoscope for routine tracheal intubation in high-risk cardio-surgical patients

<p>Abstract</p> <p>Background</p> <p>The Airtraq^®optical laryngoscope (Prodol Ltd., Vizcaya, Spain) is a novel disposable device facilitating tracheal intubation in routine and difficult airway patients. No data investigating routine tracheal intubation using the Airtaq^®in patients at a high cardiac risk are available at present. Purpose of this study was to investigate the feasibility and hemodynamic implications of tracheal intubation with the Aitraq^®optical laryngoscope, in high-risk cardio-surgical patients.</p> <p>Methods</p> <p>123 consecutive ASA III patients undergoing elective coronary artery bypass grafting were routinely intubated with the Airtraq^®laryngoscope. Induction of anesthesia was standardized according to our institutional protocol. All tracheal intubations were performed by six anesthetists trained in the use of the Airtraq^®prior.</p> <p>Results</p> <p>Overall success rate was 100% (n = 123). All but five patients trachea could be intubated in the first attempt (95,9%). 5 patients were intubated in a 2nd (n = 4) or 3rd (n = 1) attempt. Mean intubation time was 24.3 s (range 16-128 s). Heart rate, arterial blood pressure and SpO₂were not significantly altered. Minor complications were observed in 6 patients (4,8%), i.e. two lesions of the lips and four minor superficial mucosal bleedings. Intubation duration (p = 0.62) and number of attempts (p = 0.26) were independent from BMI and Mallampati score.</p> <p>Conclusion</p> <p>Tracheal intubation with the Airtraq^®optical laryngoscope was feasible, save and easy to perform in high-risk patients undergoing cardiac surgery. In all patients, a sufficient view on the vocal cords could be obtained, independent from BMI and preoperative Mallampati score.</p> <p>Trial Registration</p> <p>DRKS 00003230</p

Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian computation.

Motivation: Approximate Bayesian computation (ABC) is an increasingly popular method for likelihood-free parameter inference in systems biology and other fields of research, as it allows analyzing complex stochastic models. However, the introduced approximation error is often not clear. It has been shown that ABC actually gives exact inference under the implicit assumption of a measurement noise model. Noise being common in biological systems, it is intriguing to exploit this insight. But this is difficult in practice, as ABC is in general highly computationally demanding. Thus, the question we want to answer here is how to efficiently account for measurement noise in ABC.Results: We illustrate exemplarily how ABC yields erroneous parameter estimates when neglecting measurement noise. Then, we discuss practical ways of correctly including the measurement noise in the analysis. We present an efficient adaptive sequential importance sampling-based algorithm applicable to various model types and noise models. We test and compare it on several models, including ordinary and stochastic differential equations, Markov jump processes and stochastically interacting agents, and noise models including normal, Laplace and Poisson noise. We conclude that the proposed algorithm could improve the accuracy of parameter estimates for a broad spectrum of applications

Evaluation of derivative-free optimizers for parameter estimation in systems biology.

Derivative-free optimization can be used to estimate parameters without computing derivatives. As there exist many methods, it is unclear which to use in practice. Here, we present two comparative studies of 18 state-of-the-art methods: Firstly, we evaluate them on a set of 466 classic optimization test problems of dimension 2 to 300. Secondly, we study their performance in parameter estimation on 8 ODE models of biological processes, comparing them to state-of-the-art derivative-based optimization. We observe that different problem features necessitate the use of different methods, for which we can give suggestions based on our findings. Our analysis suggests that classic test problems are not representative for problems in systems biology. For ODE models, we find that purely derivative-free methods are for most problems not reliable or at least inferior to derivative-based methods

Benchmarking of numerical integration methods for ODE models of biological systems.

Ordinary differential equation (ODE) models are a key tool to understand complex mechanisms in systems biology. These models are studied using various approaches, including stability and bifurcation analysis, but most frequently by numerical simulations. The number of required simulations is often large, e.g., when unknown parameters need to be inferred. This renders efficient and reliable numerical integration methods essential. However, these methods depend on various hyperparameters, which strongly impact the ODE solution. Despite this, and although hundreds of published ODE models are freely available in public databases, a thorough study that quantifies the impact of hyperparameters on the ODE solver in terms of accuracy and computation time is still missing. In this manuscript, we investigate which choices of algorithms and hyperparameters are generally favorable when dealing with ODE models arising from biological processes. To ensure a representative evaluation, we considered 142 published models. Our study provides evidence that most ODEs in computational biology are stiff, and we give guidelines for the choice of algorithms and hyperparameters. We anticipate that our results will help researchers in systems biology to choose appropriate numerical methods when dealing with ODE models

Inferring the effect of interventions on COVID-19 transmission networks.

Countries around the world implement nonpharmaceutical interventions (NPIs) to mitigate the spread of COVID-19. Design of efficient NPIs requires identification of the structure of the disease transmission network. We here identify the key parameters of the COVID-19 transmission network for time periods before, during, and after the application of strict NPIs for the first wave of COVID-19 infections in Germany combining Bayesian parameter inference with an agent-based epidemiological model. We assume a Watts–Strogatz small-world network which allows to distinguish contacts within clustered cliques and unclustered, random contacts in the population, which have been shown to be crucial in sustaining the epidemic. In contrast to other works, which use coarse-grained network structures from anonymized data, like cell phone data, we consider the contacts of individual agents explicitly. We show that NPIs drastically reduced random contacts in the transmission network, increased network clustering, and resulted in a previously unappreciated transition from an exponential to a constant regime of new cases. In this regime, the disease spreads like a wave with a finite wave speed that depends on the number of contacts in a nonlinear fashion, which we can predict by mean field theory

AMICI: High-performance sensitivity analysis for large ordinary differential equation models.

SUMMARY: Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C ++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification. AVAILABILITY: AMICI is published under the permissive BSD-3-Clause license with source code publicly available on https://github.com/AMICI-dev/AMICI. Citeable releases are archived on Zenodo. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

HCV spread kinetics reveal varying contributions of transmission modes to infection dynamics.

The hepatitis C virus (HCV) is capable of spreading within a host by two different transmission modes: cell-free and cell-to-cell. However, the contribution of each of these transmission mechanisms to HCV spread is unknown. To dissect the contribution of these different transmission modes to HCV spread, we measured HCV lifecycle kinetics and used an in vitro spread assay to monitor HCV spread kinetics after a low multiplicity of infection in the absence and presence of a neutralizing antibody that blocks cell-free spread. By analyzing these data with a spatially explicit mathematical model that describes viral spread on a single-cell level, we quantified the contribution of cell-free, and cell-to-cell spread to the overall infection dynamics and show that both transmission modes act synergistically to enhance the spread of infection. Thus, the simultaneous occurrence of both transmission modes represents an advantage for HCV that may contribute to viral persistence. Notably, the relative contribution of each viral transmission mode appeared to vary dependent on different experimental conditions and suggests that viral spread is optimized according to the environment. Together, our analyses provide insight into the spread dynamics of HCV and reveal how different transmission modes impact each other