RESUMEN
The COVID-19 sequelae have been shown to affect respiratory and cardiological functions as well as neuro-psychological functions, and, in some cases, metabolic/nutritional aspects. The Italian National Institute for Insurance against Accidents at Work (Istituto Nazionale Assicurazione Infortuni sul Lavoro, INAIL) recorded that, until December 2022, 315,055 workers were affected by COVID-19; therefore, there is a need to identify an effective approach to treat such patients. Robotic and technological devices could be integrated into the rehabilitation programme of people with long COVID conditions. A review of the literature showed that telerehabilitation may improve functional capacity, dyspnoea, performance, and quality of life in these patients, but no studies were found evaluating the effects of robot-mediated therapy or virtual reality systems. Considering the above, Fondazione Don Carlo Gnocchi and INAIL propose a multi-axial rehabilitation for workers with COVID-19 sequelae. To accomplish this goal, the two institutions merged the epidemiological information gathered by INAIL, the expertise in robotic and technological rehabilitation of Fondazione Don Carlo Gnocchi, and the literature review. Our proposal aims to facilitate a multi-axial rehabilitation approach customized to meet the unique needs of each individual, with a particular emphasis on utilizing advanced technologies to address the current and future challenges of patient care.
RESUMEN
To date, the relationship between central hallmarks of multiple sclerosis (MS), such as white matter (WM)/cortical demyelinated lesions and cortical gray matter atrophy, remains unclear. We investigated the interplay between cortical atrophy and individual lesion-type patterns that have recently emerged as new radiological markers of MS disease progression. We employed a machine learning model to predict mean cortical thinning in whole-brain and single hemispheres in 150 cortical regions using demographic and lesion-related characteristics, evaluated via an ultrahigh field (7 Tesla) MRI. We found that (i) volume and rimless (i.e., without a "rim" of iron-laden immune cells) WM lesions, patient age, and volume of intracortical lesions have the most predictive power; (ii) WM lesions are more important for prediction when their load is small, while cortical lesion load becomes more important as it increases; (iii) WM lesions play a greater role in the progression of atrophy during the latest stages of the disease. Our results highlight the intricacy of MS pathology across the whole brain. In turn, this calls for multivariate statistical analyses and mechanistic modeling techniques to understand the etiopathogenesis of lesions.
