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BackgroundPrompt and proficient basic life support (BLS) maneuvers are essential to increasing the odds of survival after out-of-hospital cardiac arrest. However, significant time can elapse before the arrival of professional rescuers. To decrease these delays, many countries have developed first responder networks. These networks are composed of BLS-certified lay or professional rescuers who can be dispatched by emergency medical communication centers to take care of those who experience out-of-hospital cardiac arrest. Many systems are, however, limited by a relatively low number of active first responders, and first-year medical and dental students may represent an almost untapped pool of potential rescuers. On top of providing an enhanced BLS coverage to the population, this could also help medical students be better prepared to their future role as certified health care providers and address societal expectations regarding health care students. ObjectiveOur objective was to describe the impact of a short motivational intervention followed by a blended BLS course (e-learning and practice session) designed to motivate first-year medical and dental students to enlist as first responders. MethodsA short, web-based, motivational intervention presenting this project took place, and first-year University of Geneva, Faculty of Medicine students were provided with a link to the study platform. Those who agreed to participate were redirected to a demographic questionnaire before registering on the platform. The participants were then asked to answer a second questionnaire designed to determine their baseline knowledge prior to following an interactive e-learning module. Upon completion, a web-based booking form enabling them to register for a 1-hour practice session was displayed. These sessions were held by senior medical students who had been trained and certified as BLS instructors. The participants who attended these practice sessions were asked to answer a postcourse questionnaire before receiving the certificate enabling them to register as first responders. ResultsOut of the 529 first-year students registered at University of Geneva, Faculty of Medicine on January 14, 2021, 190 (35.9%) initially agreed to participate. Moreover, 102 (19.3%) attended the practice sessions, and 48 (9.1%) had completed all training and enlisted as first responders on the dedicated platform, Save a Life, at 6 months (July 14, 2021). Postcourse confidence in resuscitation skills was associated with a higher likelihood of registering as first responder (P=.03). No association was found between prior BLS knowledge and the probability of registering to a practice session (P=.59), of obtaining a course completion certificate (P=.29), or of enlisting as first responder (P=.56). ConclusionsThis study shows that a motivational intervention associated with a short BLS course can convince medical students to enlist as first responders. Further studies are needed to understand the rather low proportion of medical students finally registering as first responders. International Registered Report Identifier (IRRID)RR2-10.2196/24664

Abstract Introduction ALK and ROS1 rearrangements are molecular targets of several tyrosine kinase inhibitors. RNA‐sequencing approaches are regarded as the new standard for fusion gene detection, representing an alternative to standard immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) techniques. Patients and Methods We aimed to compare two recent amplicon‐based RNA‐sequencing techniques: FusionPlex® Alk Ret Ros1 v2 Kit (Archer®) with FHS‐003Z‐12—Human Lung Cancer Panel (Qiagen®) and assessed the accuracy of the data for therapy management. Thirty‐seven formalin‐fixed paraffin‐embedded non‐small cell carcinoma (NSCC) lesions initially explored by IHC and FISH were selected for RNA‐sequencing analysis. Results Qiagen® and Archer® kits produced similar results and correctly identified 85.1% (23/27) and 81.5% (22/27) of IHC/FISH ALK‐ and ROS1‐positive samples, respectively, and 100% (6/6) of the negative samples. With regard to the ambiguous IHC‐positive/FISH‐negative cases, RNA‐sequencing confirmed 75% (3/4) of the FISH conclusion. Although not statistically significant, patients with common EML4‐ALK variants presented shorter overall survival and progression‐free survival compared with patients harboring rare variants. Conclusion Our findings assessed the implementation of RNA‐sequencing approaches to explore ALK and ROS1 rearrangements from formalin‐fixed paraffin‐embedded samples. We highlighted the similarities between Qiagen® and Archer® kits in terms of handling time, cost, and outcomes. We confirmed the feasibility of molecular testing in routine organization and its possible use not only as an alternative for standard IHC and FISH techniques, but as a supplementary technique helping to classify discrepant cases.

Abstract Electrical impedance tomography is a modern biomedical imaging method. Its goal is to image the electrical properties of human tissues. This approach is safe for the patient’s health, is non-invasive and has no known hazards. However, the approach suffers from low accuracy. Linear inverse solvers are commonly used in medical applications, as they are strongly robust to noise. However, linear methods can give only an approximation of the solution that corresponds to a linear perturbation from an initial estimate. This paper proposes a novel reconstruction process. After applying a linear solver, the conductivity distribution is post-processed with a nonlinear algorithm, with the aim of reproducing the abrupt change in conductivity at the boundaries between tissues or organs. The results are used to compare the proposed method with three other widely used methods. The proposed method offers higher quality images and a higher robustness to noise, and significantly reduces the error associated with image reconstruction.

Electrical Impedance Tomography (EIT) is a powerful non-invasive technique for imaging applications. The goal is to estimate the electrical properties of living tissues by measuring the potential at the boundary of the domain. Being safe with respect to patient health, non-invasive, and having no known hazards, EIT is an attractive and promising technology. However, it suffers from a particular technical difficulty, which consists of solving a nonlinear inverse problem in real time. Several nonlinear approaches have been proposed as a replacement for the linear solver, but in practice very few are capable of stable, high-quality, and real-time EIT imaging because of their very low robustness to errors and inaccurate modeling, or because they require considerable computational effort.In this paper, a post-processing technique based on an artificial neural network (ANN) is proposed to obtain a nonlinear solution to the inverse problem, starting from a linear solution. While common reconstruction methods based on ANNs estimate the solution directly from the measured data, the method proposed here enhances the solution obtained from a linear solver.Applying a linear reconstruction algorithm before applying an ANN reduces the effects of noise and modeling errors. Hence, this approach significantly reduces the error associated with solving 2D inverse problems using machine-learning-based algorithms.This work presents radical enhancements in the stability of nonlinear methods for biomedical EIT applications.