Publications

Background and objectives: Late-onset Pompe disease (LOPD) is the first genetic neuromuscular disease treated with enzyme replacement therapy (ERT) in 2006, with variable results over time. This study aimed to assess therapeutic efficacy and safety in a large national cohort of patients after 10 and 15 years of treatment with alglucosidase alfa, all of them regularly evaluated in expert Centers. Methods: This retrospective study analyzed data from 15 Italian Centers, examining clinical-genetic features and motor and respiratory outcomes at baseline, 10 years (T10, n = 85), and 15 years (T15, n = 42) after ERT initiation. Patients were categorized by baseline 6-min walk test (6MWT: 1: < 150 m, 2: 150-299 m, 3: 300–449 m, 4: ≥ 450 m) or forced vital capacity (FVC: 0: < 80%, 1: ≥ 80%) to assess outcome differences based on initial functional status. Results: All patients were ambulant at baseline. Motor performance, assessed by 6MWT, declined across all functional groups, but even the lowest-performing patients at baseline (Groups 1–2) were mostly ambulant by T15 (50% and 71% respectively). In the best performing patients at baseline (Group 4), subjects maintained quite high performance values also at T15, with a statistically significant decrement observed at T10, and a stabilization at T15; none of them lost ambulation at T15. Despite an overall FVC% reduction, 21/42 patients (50%) remained ventilator-free at T15. No ERT discontinuations or significant adverse events were reported. Conclusion: Alglucosidase alfa therapy showed variable results in a long-term perspective, confirming a reduction in mortality in all functional groups, and stabilization in several patients, without relevant safety concerns. Motor and respiratory function responses varied by functional groups and in single patients, underscoring the need for additional outcome measures. These long-term results will be useful for comparing the possible prolonged efficacy of the new therapies for Pompe disease.

Apraxia is a neuropsychological disorder that impairs voluntary, purposeful movements, with “Gait apraxia” specifically affecting walking. Because of the lack of standardized diagnostic tools, in Italian, we translated and adapted the “Assessment of Walking Skills” (AWS) scale, originally developed for identifying gait apraxia in Alzheimer’s patients. The AWS includes 42 items that evaluate trunk and leg movements, useful for comparing the performance between patients and healthy controls. This translation and adaptation represent a critical step toward standardizing the AWS scale for the Italian population.

Background: Facioscapolohumeral muscular dystrophy is characterized by a wide clinical variability; the underlying reasons and the relation between them and the genetic markers are still not clear. In fact, the different phenotypes could show a different disease progression and/or imply distinct genetic mechanisms. As clinical trials are approaching also for FSHD, the correct description and stratification of patients becomes mandatory. To address these matters, in 2016 the Italian Clinical Group for FSHD developed the Comprehensive Clinical Evaluation Form (CCEF), aimed at describing the different observed phenotypes in FSHD. Methods: A working group composed by the former developers of the CCEF and other expert clinicians re-evaluated the whole structure of the CCEF, to develop a simplified version for use in clinical practice; also, other expert clinicians not referring to the Italian Clinical Group for FSHD read and approved the CCEF revised version for its international use. Results: We present the CCEF-R, a revised and simplified version of the CCEF, that while maintaining all the core structure and items of the previous validated version, has been modified with new friendlier graphics, focused on the key anamnestic and neurological examination findings, to facilitate its understanding and use in clinical practice. Conclusions: The phenotypical classification combined with the genetic signature should be considered during the diagnostic work out for guiding genetic analysis and for genotype–phenotype correlations and genetic counseling. The CCEF could have a significant role in the clinical stratification process of patients for clinical trials and in laying the groundwork for evidence-based medical decision making.

Motor imagery (MI) is a fundamental brain-machine interface (BMI) paradigm in which users learn how to modulate their brain signals to voluntarily and selectively activate specific areas of the sensorimotor cortex. The self-paced nature of this kinesthetic imagination makes MI well-suited for several human-robot interaction (HRI) scenarios. However, the performance of MI decoding is highly dependent on both the user’s expertise and the quality of the decoding algorithm. To address these challenges, we propose a method that integrates environmental data from robotic sensors to enhance the MI decoding process. Preliminary experiments indicate that this approach improves decoding accuracy and the overall performance of the brain-driven system, opening new opportunities for research on how machines can enhance the usability of BMIs systems.

Ordinary Portland cement (OPC) is a cost-effective and conventional binder that is widely adopted in brownfield site remediation and redevelopment. However, the substantial carbon dioxide emission during OPC production and the concerns about its undesirable retention capacity for potentially toxic elements strain this strategy. To tackle this objective, we herein tailored four alternative binders (calcium aluminate cement, OPC-activated ground-granulated blast-furnace slag (GGBFS), white-steel-slag activated GGBFS, and alkaline-activated GGBFS) for facilitating immobilization of high Pb content pyrite ash, with the perspectives of enhancing Pb retention and mitigating anthropogenic carbon dioxide emissions. The characterizations revealed that the incorporation of white steel slag efficiently benefits the activity of GGBFS, herein facilitating the hydration products (mainly ettringite and calcium silicate hydrates) precipitation and Pb immobilization. Further, we quantified the cradle-to-gate carbon footprint and cost analysis attributed to each binder-Pb contaminants system, finding that the application of these alternative binders could be pivotal in the envisaged carbon-neutral world if the growth of the OPC-free roadmap continues. The findings suggest that the synergistic use of recycled white steel slag and GGBFS can be proposed as a profitable and sustainable OPC-free candidate to facilitate the management of lead-contaminated brownfield sites. The overall results underscore the potential immobilization mechanisms of Pb in multiple OPC-free/substitution binder systems and highlight the urgent need to bridge the zero-emission insights to sustainable in-situ solidification/stabilization technologies.

Autonomous mobile robots can rely on several human motion detection and prediction systems for safe and efficient navigation in human environments, but the underline model architectures can have different impacts on the trustworthiness of the robot in the real world. Among existing solutions for context-aware human motion prediction, some approaches have shown the benefit of integrating symbolic knowledge with state-of-the-art neural networks. In particular, a recent neuro-symbolic architecture (NeuroSyM) has successfully embedded context with a Qualitative Trajectory Calculus (QTC) for spatial interactions representation. This work achieved better performance than neural-only baseline architectures on offline datasets. In this paper, we extend the original architecture to provide neuROSym, a ROS package for robot deployment in real-world scenarios, which can run, visualise, and evaluate previous neural-only and neuro-symbolic models for motion prediction online. We evaluated these models, NeuroSyM and a baseline SGAN, on a TIAGo robot in two scenarios with different human motion patterns. We assessed accuracy and runtime performance of the prediction models, showing a general improvement in case our neuro-symbolic architecture is used. We make the neuROSym package 1 publicly available to the robotics community.

Deploying robots in human-shared environments requires a deep understanding of how nearby agents and objects interact. Employing causal inference to model cause-and-effect relationships facilitates the prediction of human behaviours and enables the anticipation of robot interventions. However, a significant challenge arises due to the absence of implementation of existing causal discovery methods within the ROS ecosystem, the standard de-facto framework in robotics, hindering effective utilisation on real robots. To bridge this gap, in our previous work we proposed ROS-Causal, a ROS-based framework designed for onboard data collection and causal discovery in human-robot spatial interactions. In this work, we present an experimental evaluation of ROS-Causal both in simulation and on a new dataset of human-robot spatial interactions in a lab scenario, to assess its performance and effectiveness. Our analysis demonstrates the efficacy of this approach, showcasing how causal models can be extracted directly onboard by robots during data collection. The online causal models generated from the simulation are consistent with those from lab experiments. These findings can help researchers to enhance the performance of robotic systems in shared environments, firstly by studying the causal relations between variables in simulation without real people, and then facilitating the actual robot deployment in real human environments. ROS-Causal: https://lcastri.github.io/roscausal

The study of cause and effect is of the utmost importance in many branches of science, but also for many practical applications of intelligent systems. In particular, identifying causal relationships in situations that include hidden factors is a major challenge for methods that rely solely on observational data for building causal models. This article proposes CAnDOIT, a causal discovery method to reconstruct causal models using both observational and interventional time-series data. The use of interventional data in the causal analysis is crucial for real-world applications, such as robotics, where the scenario is highly complex and observational data alone are often insufficient to uncover the correct causal structure. Validation of the method is performed initially on randomly generated synthetic models and subsequently on a well-known benchmark for causal structure learning in a robotic manipulation environment. The experiments demonstrate that the approach can effectively handle data from interventions and exploit them to enhance the accuracy of the causal analysis. A Python implementation of CAnDOIT is developed and is publicly available on GitHub: https://github.com/lcastri/causalflow.