A low-cost, DIY tourniquet simulator with built-in self-assessment for prehospital providers in Guatemala city

BACKGROUND

Hemorrhage is the leading cause of preventable death after trauma. In high-income countries first responders are trained in hemorrhage control techniques but this is not the case for developing countries like Guatemala. We present a low-cost training model for tourniquet application using a combination of virtual and physical components.

METHODS

The training program includes a mobile application with didactic materials, videos and a gamified virtual reality environment for learning. Additionally, a physical training model of a bleeding lower extremity is developed allowing learners to practice tourniquet application using inexpensive and accessible materials. Validation of the simulator occurred through content and construct validation. Content validation involved subjective assessments by novices and experts, construct validation compared pre-training novices with experts. Training validation compared pre and post training novices for improvement.

RESULTS

Our findings indicate that users found the simulator useful, realistic, and satisfactory. We found significant differences in tourniquet application skills between pre-training novices and experts. When comparing pre- and post-training novices, we found a significantly lower bleeding control time between the groups.

CONCLUSION

This study suggests that this training approach can enhance access to life-saving skills for prehospital personnel. The inclusion of self-assessment components enables self-regulated learning and reduces the need for continuous instructor presence. Future improvements involve refining the tourniquet model, validating it with first-responder end users, and expanding the training program to include other skills.

Deep Learning-Based Image Registration in Dynamic Myocardial Perfusion CT Imaging

Registration of dynamic CT image sequences is a crucial preprocessing step for clinical evaluation of multiple physiological determinants in the heart such as global and regional myocardial perfusion. In this work, we present a deformable deep learning-based image registration method for quantitative myocardial perfusion CT examinations, which in contrast to previous approaches, takes into account some unique challenges such as low image quality with less accurate anatomical landmarks, dynamic changes of contrast agent concentration in the heart chambers and tissue, and misalignment caused by cardiac stress, respiration, and patient motion. The introduced method uses a recursive cascade network with a ventricle segmentation module, and a novel loss function that accounts for local contrast changes over time. It was trained and validated on a dataset of n = 118 patients with known or suspected coronary artery disease and/or aortic valve insufficiency. Our results demonstrate that the proposed method is capable of registering dynamic cardiac perfusion sequences by reducing local tissue displacements of the left ventricle (LV), whereas contrast changes do not affect the registration and image quality, in particular the absolute CT (HU) values of the entire CT sequence. In addition, the deep learning-based approach presented reveals a short processing time of a few seconds compared to conventional image registration methods, demonstrating its application potential for quantitative CT myocardial perfusion measurements in daily clinical routine.

Salmonella typhimurium gyrA mutations associated with fluoroquinolone resistance

Spontaneous quinolone-resistant mutants obtained from Salmonella typhimurium Su694 were screened for mutations by direct DNA sequencing of an amplified PCR gyrA fragment. Substitutions Ser-83-->Phe (Ser83Phe), Ser83Tyr, Asp87Tyr, and Asp87Asn and double mutation Ala67Pro-Gly81Ser, which resulted in decreased sensitivities to ciprofloxacin, enoxacin, pefloxacin, norfloxacin, ofloxacin, and nalidixic acid, were found. The levels of resistance to quinolones for each mutant were determined.