Publications

Myofibrillar Myopathies (MFMs) are a growing group of muscular disorders genetically determined, whose diagnosis is based on histological features as myofibrillar degeneration, Z-disk disorganization and protein aggregates’ accumulation. Protein aggregates that do not fit the proteasome’s narrow pore are targeted for removal via a specialized form of autophagy, called aggrephagy. Our study aims to investigate the potential pathogenic role of aggrephagy in 52 muscle samples from an Italian MFM multicentric cohort. We measured, the percentage of positive areas of key aggrephagy proteins by immunofluorescence staining, of sequestosome 1 (p62/SQSTM1), Neighbor of BRCA1 Gene 1 (NBR1), and ubiquitinated proteins (FK2) in 11 DES-, 6 DNAJB6-, 5 FLNC-, 18 MYOT- and 12 TTN-mutated patients. We showed that all aggrephagy markers are increased in these patients, regardless of the mutated genes, suggesting a possible common pathomechanism; no positive signal was found in healthy, age-matched controls. We analyzed the association between positivity levels of these markers, measured as percentage of positive areas, and selected clinical features utilizing generalized linear mixed models with gamma distribution as the probability model and center-specific random effects to better capture possible heterogeneity across participating centers. Our findings indicate significant associations between levels of p62, NBR1, and FK2 with age at biopsy (p62 and NBR1 p-values < 0.001, FK2 p-value < 0.05), age of onset (p62 and NBR1 p-values < 0.001, FK2 p-value < 0.01) and disease severity through Walton & Gardner-Medwin (WGM) score at biopsy (all p-values < 0.001) and at the last visit (all p-values < 0.05). Noteworthy, the aggrephagic pathway is mostly activated in MYOT-mutated patients compared to the other subgroups. Moreover, the association between aggrephagy and WGM score at biopsy is stronger in this subgroup. Overall, our study emphasizes the role of aggrephagy in MFMs across all patients, and its association with specific clinical parameters.

Electroencephalography (EEG) based brain-computer interfaces (BCIs) offer a promising way for individuals with motor impairments to control prosthetic or rehabilitation devices. Accurately decoding movement intention (MI) is crucial for translating subjects’ motor execution plans into action. Common challenges in EEG-based BCIs include performance discrepancies, often requiring frequent recalibration of decoding algorithms. The objective of this study was enhancing BCI decoding performance of upper-limb MI identification by exploiting both machine and subjects’ learning and maintaining stable decoding algorithms. Significant performance improvements were observed across most subjects from the first to the last session of the experiment. Some subjects also demonstrated stable performance without requiring any model recalibration between sessions. All subjects achieved high efficacy in online decoding of movement intention, as reflected in improvement of the F1 score from 0.58±0.26 in the first session, to 0.84±0.13 in the final session. We emphasize the critical importance of allowing users sufficient time to improve their performance in BCIs for upper-limb MI decoding. Unlike existing studies, we specifically evaluate the effect of stable decoding strategies in online and longitudinal BCI sessions, which are key to achieving more reliable and effective BCIs.

Open vocabulary 3D object detection (OV3D) allows precise and extensible object recognition crucial for adapting to diverse environments encountered in assistive robotics. This paper presents OpenNav, a zero-shot 3D object detection pipeline based on RGB-D images for smart wheelchairs. Our pipeline integrates an open-vocabulary 2D object detector with a mask generator for semantic segmentation, followed by depth isolation and point cloud construction to create 3D bounding boxes. The smart wheelchair exploits these 3D bounding boxes to identify potential targets and navigate safely. We demonstrate OpenNav’s performance through experiments on the Replica dataset and we report preliminary results with a real wheelchair. OpenNav improves state-of-the-art significantly on the Replica dataset at mAP25 (+9pts) and mAP50 (+5pts) with marginal improvement at mAP. The code is publicly available at this link https://github.com/EasyWalk-PRIN/OpenNav.

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