Age-dependent motor unit remodelling in human limb muscles.

Voluntary control of skeletal muscle enables humans to interact with and manipulate the environment. Lower muscle mass, weakness and poor coordination are common complaints in older age and reduce physical capabilities. Attention has focused on ways of maintaining muscle size and strength by exercise, diet or hormone replacement. Without appropriate neural innervation, however, muscle cannot function. Emerging evidence points to a neural basis of muscle loss. Motor unit number estimates indicate that by age around 71 years, healthy older people have around 40 % fewer motor units. The surviving low- and moderate-threshold motor units recruited for moderate intensity contractions are enlarged by around 50 % and show increased fibre density, presumably due to collateral reinnervation of denervated fibres. Motor unit potentials show increased complexity and the stability of neuromuscular junction transmissions is decreased. The available evidence is limited by a lack of longitudinal studies, relatively small sample sizes, a tendency to examine the small peripheral muscles and relatively few investigations into the consequences of motor unit remodelling for muscle size and control of movements in older age. Loss of motor neurons and remodelling of surviving motor units constitutes the major change in ageing muscles and probably contributes to muscle loss and functional impairments. The deterioration and remodelling of motor units likely imposes constraints on the way in which the central nervous system controls movements

TCT-454 Complications and Failure Modes of Inari FlowTriever Aspiration System in Pulmonary Embolism: Insights From the MAUDE Database

Background: The Inari FlowTriever aspiration system (Inari Medical) gained US Food and Drug Administration approval in May 2018 for use in pulmonary embolism. Data on its failure mechanisms are limited. Methods: We investigated the Manufacturer and User Facility Device Experience database for reports on Inari FlowTriever aspiration system failure from June 2018 to May 2021. The outcomes of this study were the device failure modes and their clinical consequences. Results: A total of 27 reports were found during the study period. After excluding duplicate reports (n = 6), incomplete ones (n = 1), and those of deep venous thrombosis without pulmonary embolism (n = 4), our final cohort included 16 reports. Injury to the pulmonary vessels occurred in 6 reports (37.5%), with pulmonary perforation being the most common type of injury, occurring in 3 reports (18.8%), followed by pulmonary pseudoaneurysm in 2 reports (12.5%) and pulmonary dissection in 1 report (6.3%). Hemoptysis occurred in 4 reports (25%) and pericardial effusion in 3 reports (18.8%), and blood transfusion was needed in 5 reports (31.3%). Cardiopulmonary arrest occurred in 11 (68.8%) and death in 10 (62.5%) reports. Conclusion: We found that reports of Inari FlowTriever Aspiration System failure were rare (16 over 3 years). However, failure reports were serious and included pulmonary artery injury and hemoptysis. Careful catheter manipulation may help avoid such complications

Federated learning for predicting clinical outcomes in patients with COVID-19

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare