IAS-LAB PUBLICATIONS
Muscle inflammatory pattern in alpha- and gamma-sarcoglycanopathies
Authors: Panicucci Chiara; Baratto Serena; Raffaghello Lizzia; Tonin Paola; D'Amico Adele; Tasca Giorgio; Traverso Monica; Fiorillo Chiara; Minetti Carlo; Previtali Stefano Carlo; Pegoraro Elena; Bruno Claudio
Journal: CLINICAL NEUROPATHOLOGY
Published: 2021
DOI: 10.5414/NP301393
Aim: Since the immune system plays a role in the pathogenesis of several muscular dystrophies, we aim to characterize several muscular inflammatory features in α- (LGMD R3) and γ-sarcoglycanopathies (LGMD R5). Materials and methods: We explored the expression of major histocompatibility complex class I molecules (MHCI), and we analyzed the composition of the immune infiltrates in muscle biopsies from 10 patients with LGMD R3 and 8 patients with LGMD R5, comparing the results to Duchenne muscular dystrophy patients (DMD). Results: A consistent involvement of the immune response was observed in sarcoglycanopathies, although it was less evident than in DMD. LGMD R3-R5 and DMD shared an abnormal expression of MHCI, and the composition of the muscular immune cell infiltrate was comparable. Conclusion: These findings might serve as a rationale to fine-tune a disease-specific immunomodulatory regimen, particularly relevant in view of the rapid development of gene therapy for sarcoglycanopathies.
Volume: 40 Pages: 310-318
Keywords: DAMPS; Duchenne muscular dystrophy; inflammation; limb-girdle muscular dystrophies; sarcoglycanopathy;
Next-generation sequencing application to investigate skeletal muscle channelopathies in a large cohort of Italian patients
Authors: Brugnoni Raffaella; Maggi Lorenzo; Canioni Eleonora; Verde Federico; Gallone Annamaria; Ariatti Alessandra; Filosto Massimiliano; Petrelli Cristina; Logullo Francesco Ottavio; Esposito Marcello; Ruggiero Lucia; Tonin Paola; Riguzzi Pietro; Pegoraro Elena; Torri Francesca; Ricci Giulia; Siciliano Gabriele; Silani Vincenzo; Merlini Luciano; De Pasqua Silvia; Liguori Rocco; Pini Antonella; Mariotti Caterina; Moroni Isabella; Imbrici Paola; Desaphy Jean-Francois; Mantegazza Renato; Bernasconi Pia
Journal: NEUROMUSCULAR DISORDERS
Published: 2021
DOI: 10.1016/j.nmd.2020.12.003
Non-dystrophic myotonias and periodic paralyses are a heterogeneous group of disabling diseases classified as skeletal muscle channelopathies. Their genetic characterization is essential for prognostic and therapeutic purposes; however, several genes are involved. Sanger-based sequencing of a single gene is time-consuming, often expensive; thus, we designed a next-generation sequencing panel of 56 putative candidate genes for skeletal muscle channelopathies, codifying for proteins involved in excitability, excitation-contraction coupling, and metabolism of muscle fibres. We analyzed a large cohort of 109 Italian patients with a suspect of NDM or PP by next-generation sequencing. We identified 24 patients mutated in CLCN1 gene, 15 in SCN4A, 3 in both CLCN1 and SCN4A, 1 in ATP2A1, 1 in KCNA1 and 1 in CASQ1. Eight were novel mutations: p.G395Cfs*32, p.L843P, p.V829M, p.E258E and c.1471+4delTCAAGAC in CLCN1, p.K1302R in SCN4A, p.L208P in ATP2A1 and c.280–1G>C in CASQ1 genes. This study demonstrated the utility of targeted next generation sequencing approach in molecular diagnosis of skeletal muscle channelopathies and the importance of the collaboration between clinicians and molecular geneticists and additional methods for unclear variants to make a conclusive diagnosis.
Volume: 31 Pages: 336-347
Keywords: CLCN1 gene; Next-generation sequencing; Non-dystrophic myotonias; Periodic paralyses; SCN4A gene; Skeletal muscle channelopathies;
Diagnostic Developments in Differentiating Unresponsive Wakefulness Syndrome and the Minimally Conscious State
Authors: Porcaro Camillo; Nemirovsky Idan Efim; Riganello Francesco; Mansour Zahra; Cerasa Antonio; Tonin Paolo; Stojanoski Bobby; Soddu Andrea
Journal: FRONTIERS IN NEUROLOGY
Published: 2021
DOI: 10.3389/fneur.2021.778951
When treating patients with a disorder of consciousness (DOC), it is essential to obtain an accurate diagnosis as soon as possible to generate individualized treatment programs. However, accurately diagnosing patients with DOCs is challenging and prone to errors when differentiating patients in a Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) from those in a Minimally Conscious State (MCS). Upwards of ~40% of patients with a DOC can be misdiagnosed when specifically designed behavioral scales are not employed or improperly administered. To improve diagnostic accuracy for these patients, several important neuroimaging and electrophysiological technologies have been proposed. These include Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), and Transcranial Magnetic Stimulation (TMS). Here, we review the different ways in which these techniques can improve diagnostic differentiation between VS/UWS and MCS patients. We do so by referring to studies that were conducted within the last 10 years, which were extracted from the PubMed database. In total, 55 studies met our criteria (clinical diagnoses of VS/UWS from MCS as made by PET, fMRI, EEG and TMS- EEG tools) and were included in this review. By summarizing the promising results achieved in understanding and diagnosing these conditions, we aim to emphasize the need for more such tools to be incorporated in standard clinical practice, as well as the importance of data sharing to incentivize the community to meet these goals.
Volume: 12
Keywords: disorder of consciousness (DOC); functional magnetic resonance imaging (fMRI); magneto-electroencephalography (M-EEG); minimally conscious state (MCS); positron emission tomography (PET); transcranial magnetic stimulation (TMS); unresponsiveness wakefulness syndrome (UWS); vegetative state (VS);
A Systematic Review on Motor-Imagery Brain-Connectivity-Based Computer Interfaces
Authors: Brusini Lorenza; Stival Francesca; Setti Francesco; Menegatti Emanuele; Menegaz Gloria; Storti Silvia Francesca
Journal: IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
Published: 2021
DOI: 10.1109/THMS.2021.3115094
This review article discusses the definition and implementation of brain-computer interface (BCI) system relying on brain connectivity (BC) and machine learning/deep learning (DL) for motor imagery (MI)-based applications. During the past few years, many approaches have been explored in terms of types of neurological sources of information, feature extraction, and intention prediction for BCI applications. Two novel aspects are becoming increasingly interesting for the BCI community: BC modeling and DL. The former aims at describing the interactions among different brain regions as connectivity patterns that reflect the dynamics of information flow either at rest or when performing a task. The latter is becoming pervasive for its capability of modeling and predicting complex data, where a huge amount of information is involved. In this scenario, we conducted a systematic literature review on BCI studies that led to the selection of 34 articles meeting all the required criteria. This provides evidence of the rapid growth of the topic over the past few years, though being still in its infancy. The last part of this article is dedicated to this new frontier of BCI that we call MI BC-based computer interfaces highlighting the potential of BC features. This, jointly with DL as enabling technology, has the potential of improving the performance of electroencephalography-based systems.
Volume: 51 Pages: 725-733
Keywords: brain connectivity (BC); Brain-computer interface (BCI); deep learning (DL); electroencephalography (EEG); machine learning (ML); motor imagery (MI);
A ROS Driver for Xsens Wireless Inertial Measurement Unit Systems
Authors: Guidolin Mattia; Menegatti Emanuele; Reggiani Monica; Tagliapietra Luca
Journal: 2021 22ND IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT)
Published: 2021
DOI: 10.1109/ICIT46573.2021.9453640
This paper presents an efficient open-source driver for interfacing Xsens inertial measurement systems (in particular the Xsens MTw Awinda wireless motion trackers) with the Robot Operating System (ROS). The driver supports the simultaneous connection of up to 20 trackers, limit fixed by the Xsens software, to a master PC, and directly streams sensors data (linear accelerations, angular velocities, magnetic fields, orientations) up to 120 Hz to the ROS network through one or multiple configurable topics. Moreover, a synchronization procedure is implemented to avoid possible partial frames where the readings from one (or multiple) trackers are missing. The proposed messages are based on ROS standard ones and comply with the ROS developer guidelines. This guarantees the compatibility of any ROS package requiring as input ROS standard messages with the proposed driver, thus effectively integrating Xsens inertial measurement systems with the ROS ecosystem. This work aims to push forward the development of a large variety of human-robot interaction applications where accurate real-time knowledge of human motion is crucial.
Volume: 2021- Pages: 677-683
Keywords: Hi-ROS; Human-Robot Interaction; IMU; Inertial Measurement Units; ROS; Xsens;
Simple parameters from complete blood count predict in-hospital mortality in covid-19
Authors: Non assegn; AREA MIN. 06 - Scienze mediche; DISEASE MARKERS###0278-0240; Goal 3: Good health and well-being###25122; AAL-5472-2020; M-5100-2019; DUC-4101-2022; HXD-5926-2023; ABF-2102-2020; FYJ-0471-2022; AAW-2773-2021; JSK-1726-2023; CBR-3658-2022; FXI-9122-2022; CEM-4634-2022; EKF-2415-2022; CCV-8146-2022; DUQ-7461-2022; DUC-7114-2022; CDA-3646-2022; ABG-7696-2020; EPL-9094-2022; ABG-1580-2020; CFK-9692-2022; ISH-1227-2023; PHU-7009-2026; EPW-9554-2022; ELP-9603-2022; AAC-4997-2022; ENH-6142-2022; J-1094-2016; ENM-4941-2022; JGY-0806-2023; DWF-3868-2022; CJF-5787-2022; AFH-6390-2022; K-8531-2016; L-3786-2016; ENU-9422-2022; AAL-3790-2020; CHT-1202-2022; AAC-7344-2022; CKP-2390-2022; CLP-7271-2022; EPT-8379-2022; CLX-6348-2022; CLI-1343-2022; DTU-2672-2022; A-7495-2014; CMC-7014-2022; GBN-4966-2022; ABD-1193-2020; AAC-6801-2022; ESP-0272-2022; OUS-0903-2025; ETL-6905-2022; DUA-0330-2022; CQT-1172-2022; DXY-7548-2022; C-3105-2017; CQS-1140-2022; CQX-2818-2022; CQI-0842-2022; CQO-8425-2022; EXC-5059-2022; CQB-6691-2022; EWM-0981-2022; CSI-1544-2022; AAE-6666-2019; IWI-8733-2023; CSK-6771-2022; CSC-3987-2022; ECW-3234-2022; MOX-1101-2025; GQS-5751-2022; F-7703-2013; FZE-2982-2022; CWB-9162-2022; BBC-1073-2021; GCG-0683-2022; DBK-6721-2022; CZT-0541-2022; I-2688-2019; DEJ-4485-2022; GEI-3299-2022; LFK-6896-2024; GCD-3990-2022; FZQ-2807-2022; DVV-9362-2022; DEQ-3731-2022; DGH-2533-2022; GGD-9298-2022; AAC-3729-2022; FLE-0654-2022; DHB-1639-2022; HCH-8667-2022; FQR-4296-2022; GDR-4878-2022; DLO-0701-2022; FOL-4641-2022; AAE-8109-2021; KFW-0943-2024; DXE-9192-2022; KGU-2527-2024; DWR-8603-2022; FQY-2309-2022; DLP-3148-2022; GCO-8303-2022; JWP-7327-2024; FSW-0173-2022; AAE-3161-2022; DZP-9117-2022; GAW-9077-2022; LSK-0627-2024; FWZ-4515-2022; KRI-7509-2024; DRS-6577-2022; ABF-2017-2020; DRV-8006-2022; ITJ-3315-2023; LML-1607-2024; AAO-6803-2021; GEC-4310-2022; GGW-1525-2022; GDB-5868-2022; JAC-7785-2023; GDA-8011-2022; GFN-0119-2022; GKZ-7885-2022; AAC-1099-2022; F-8233-2013; EIJ-9143-2022; J-1132-2016; 53879396000; 57193265972; 57216075370; 57203031145; 7004318487; 59284820200; 7003308152; 7003950516; 57196353615; 57205142127; 35396292000; 57220048069; 18039353500; 57218851883; 57201256870; 57220051467; 25654501100; 6506358478; 8722189200; 57220054836; 57220058095; 57220051634; 57220051735; 23566606800; 14036920300; 6602182910; 26431212200; 15134828400; 6603396204; 8510236600; 57211981942; 57216943836; 6602756808; 14048037400; 57220358003; 55168579500; 57218547884; 8527637700; 57220059056; 6602851393; 57218850723; 6602533927; 57211313712; 57219567830; 6701468226; 57220049466; 57220058367; 57218298082; 57218540047; 57220046791; 7005464299; 57224450738; 57203928611; 7003647087; 57213622332; 14061883600; 57216210802; 57214718431; 57219984978; 57543368200; 7006019812; 57202742726; 12239827400; 57208303340; 57202575556; 7003822754; 57213608263; 57220051169; 57224442441; 57220050467; 56536610100; 6507191683; 57218623658; 57225235365; 29467502300; 7004264305; 57220060687; 57220059521; 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57221905077; 9238242500; 57224446172; 56823454200; 57205714725; 57208295246; 57211189307; 57224442783; 48761155500; 58188358100; 59597101500; 26421201700; 55604323700; 56191860500; 57209659866; 6507944040; 57224444666; 56502076600; 56389517000; 59163683100; 57222590673; 21733692400; 55983010300; 57224451561; 55832198600; 55999235300; 55317181600; 57221726284; 57224438791; 6504341334; 24478455400; 55255226100; 6507884168; 57195526856; 6505964369; 6507774098; 56611763600; 57192804694; 55934306700; 57189644581; 57218691760; 57197937711; 57224453707; 57195519134; 6603552585; 57200142058; 57224452831; 57224445355; 57195772862; 35513148500; 57196064289; 57222590691; 57222590081; 57191277967; 57192946409; 57500821000; 6505849878; 57214559164; 7102608380; 55921637900; 6602838716; 6603163399; 35068561900; 7005916807; 55787021600; 8947797700; 57224453180; 57190742199; 57224439286; 56086850200; 59873883300; 57218668741; 36653141300; 57224450990; 36626329900; 18838235900; 55695551500; 57163372600; 57211438364; 57216075502; 59471912000; 56493025900; 35086892400; 57224443928; 6602869365; 6506968180; 6503955919; 55383836000; 57205711719; 57224450078; 12142383800; 21647473600
Journal: DISEASE MARKERS
Published: 2021
DOI: 10.1155/2021/8863053
Introduction. The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions. Materials and Methods. In this study, we retrospectively assessed the prognostic value of a simple tool, the complete blood count, on a cohort of 664 patients (F 260; 39%, median age 70 (56-81) years) hospitalized for COVID-19 in Northern Italy. We collected demographic data along with complete blood cell count; moreover, the outcome of the hospital in-stay was recorded. Results. At data cut-off, 221/664 patients (33.3%) had died and 453/664 (66.7%) had been discharged. Red cell distribution width (RDW) (χ2 10.4; p < 0:001), neutrophil-to-lymphocyte (NL) ratio (χ2 7.6; p = 0:006), and platelet count (χ2 5.39; p = 0:02), along with age (χ2 87.6; p < 0:001) and gender (χ2 17.3; p 4:68 was characterized by an odds ratio for in-hospital mortality ðORÞ = 3:40 (2.40-4.82), while the OR for a RDW > 13:7% was 4.09 (2.87-5.83); a platelet count > 166,000/μL was, conversely, protective (OR: 0.45 (0.32-0.63)). Conclusion. Our findings arise the opportunity of stratifying COVID-19 severity according to simple lab parameters, which may drive clinical decisions about monitoring and treatment.
Volume: 2021
Shared approaches to mentally drive telepresence robots
Authors: Beraldo Gloria; Tonin Luca; Cesta Amedeo; Menegatti Emanuele
Journal: 21100218356
Published: 2021
Recently there has been a growing interest in designing human-in-the-loop applications based on shared approaches that fuse the user’s commands with the perception of the context. In this scenario, we focus on user-supervised telepresence robots, designed to improve the quality of life of people suffering from severe physical disabilities or elderly who cannot move anymore. In this regard, we introduce brain-machine interfaces that enable users to directly control the robot through their brain activity. Since the nature of this interface, characterized by low bit rate and noise, herein, we present different methodologies to augment the human-robot interaction and to facilitate the research and the development of these technologies.
Volume: 2806 Pages: 22-27
Keywords: 1 neurorobotics; Behavior-based systems; Brain-machine interface; Telerobotics and teleoperation;
Cortical correlates in upright dynamic and static balance in the elderly
Authors: Rubega Maria; Formaggio Emanuela; Di Marco Roberto; Bertuccelli Margherita; Tortora Stefano; Menegatti Emanuele; Cattelan Manuela; Bonato Paolo; Masiero Stefano; Del Felice Alessandra
Journal: SCIENTIFIC REPORTS
Published: 2021
DOI: 10.1038/s41598-021-93556-3
Falls are the second most frequent cause of injury in the elderly. Physiological processes associated with aging affect the elderly’s ability to respond to unexpected balance perturbations, leading to increased fall risk. Every year, approximately 30% of adults, 65 years and older, experiences at least one fall. Investigating the neurophysiological mechanisms underlying the control of static and dynamic balance in the elderly is an emerging research area. The study aimed to identify cortical and muscular correlates during static and dynamic balance tests in a cohort of young and old healthy adults. We recorded cortical and muscular activity in nine elderly and eight younger healthy participants during an upright stance task in static and dynamic (core board) conditions. To simulate real-life dual-task postural control conditions, the second set of experiments incorporated an oddball visual task. We observed higher electroencephalographic (EEG) delta rhythm over the anterior cortex in the elderly and more diffused fast rhythms (i.e., alpha, beta, gamma) in younger participants during the static balance tests. When adding a visual oddball, the elderly displayed an increase in theta activation over the sensorimotor and occipital cortices. During the dynamic balance tests, the elderly showed the recruitment of sensorimotor areas and increased muscle activity level, suggesting a preferential motor strategy for postural control. This strategy was even more prominent during the oddball task. Younger participants showed reduced cortical and muscular activity compared to the elderly, with the noteworthy difference of a preferential activation of occipital areas that increased during the oddball task. These results support the hypothesis that different strategies are used by the elderly compared to younger adults during postural tasks, particularly when postural and cognitive tasks are combined. The knowledge gained in this study could inform the development of age-specific rehabilitative and assistive interventions.
Volume: 11
Brain-Driven Telepresence Robots: A Fusion of User’s Commands with Robot’s Intelligence
Authors: Beraldo Gloria; Tonin Luca; Cesta Amedeo; Menegatti Emanuele
Journal: AIXIA 2020 - ADVANCES IN ARTIFICIAL INTELLIGENCE
Published: 2021
DOI: 10.1007/978-3-030-77091-4_15
This paper presents different methodologies to enhance the human-robot interaction during the control of brain-machine interface (BMI) driven telepresence robots. To overcome the limitations of BMIs, namely the low bit rate and the intrinsic uncertainty as a control channel, we hypothesize that the fusion of the user’s commands with the robot’s intelligence is essential to achieve robust and natural systems. Compared to most current neurorobotics works, we exploit the robot as an intelligent agent that contributes at different levels to choose the final action to perform. Furthermore, we present the first implementation of a BMI system inside the Robot Operating System (ROS) designed to facilitate the combination between BMI and robotics.
Volume: 12414 Pages: 235-248
Keywords: Behavior-based systems; Brain-machine interface; Neurorobotics; Telerobotics and teleoperation;
Editorial: Advances in the Integration of Brain-Machine Interfaces and Robotic Devices
Authors: Tonin Luca; Menegatti Emanuele; Coyle Damien
Journal: FRONTIERS IN ROBOTICS AND AI
Published: 2021
DOI: 10.3389/frobt.2021.653615
Volume: 8
Keywords: assistive devices; Brain-machine interface; human-robot interaction; robotics; shared-control;