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Background and Objectives: In patients with COVID-19, high-flow nasal cannula (HFNC) and continuous positive airway pressure (CPAP) are widely applied as initial treatments for moderate-to-severe acute hypoxemic respiratory failure. The aim of the study was to assess which respiratory supports improve 28-day mortality and to identify a predictive index of treatment response. Materials and Methods: This is a single-center retrospective observational study including 159 consecutive adult patients with COVID-19 and moderate-to-severe hypoxemic acute respiratory failure. Results: A total of 159 patients (82 in the CPAP group and 77 in the HFNC group) were included in the study. Mortality within 28 days was significantly lower with HFNC compared to CPAP (16.8% vs. 50%), while ICU admission and tracheal intubation within 28 days were significantly higher with CPAP compared to HFNC treatment (32% vs. 13%). We identified an index for survival in HFNC by including three variables easily available at admission (LDH, age, and respiratory rate) and the PaO2/FiO2 ratio at 48 h. The index showed high discrimination for survival with an AUC of 0.88, a negative predictive value of 86%, and a positive predictive value of 95%. Conclusions: Treatment with HFNC appears to be associated with greater survival and fewer ICU admission than CPAP. LDH, respiratory rate, age, and PaO2/FiO2 at 48 h were independently associated with survival and an index based on these variables allows for the prediction of treatment success and the assessment of patient allocation to the appropriate intensity of care after 48 h. Further research is warranted to determine effects on other outcomes and to assess the performance of the index in larger cohorts.

Autologous hematopoietic stem cells transplantation (AHSCT) has been employed as treatment for severe systemic sclerosis (SSc) with high risk of organ failure. In the last 25 years overall survival and treatment-related mortality have improved, in accordance with a better patient selection and mobilization and conditioning protocols. This review analyzes the evidence from the last 5 years for AHSCT-treated SSc patients, considering in particular the outcomes related to interstitial lung disease. There are increasing data supporting the use of AHSCT in selected patients with rapidly progressive SSc. However, some unmet needs remain, such as an accurate patient selection, pre-transplantation analysis to identify subclinical conditions precluding the transplantation, and the alternatives for post-transplant ILD recurrence.

Objective: Coronavirus disease 2019 (COVID-19) is a disease with a high rate of progression to critical illness. However, the stratification of patients at risk of mortality is not well defined. In this study, we aimed to define a mortality risk index to allocate patients to the appropriate intensity of care. Methods: This is a 12 months observational longitudinal study designed to develop and validate a pragmatic mortality risk score to stratify COVID-19 patients aged ≥18 years and admitted to hospital between March 2020 and March 2021. Main outcome was in-hospital mortality. Results: 244 patients were included in the study (mortality rate 29.9%). The Covid-19 Assessment for Survival at Admission (CASA) index included seven variables readily available at admission: respiratory rate, troponin, albumin, CKD-EPI, white blood cell count, D-dimer, Pa02/Fi02. The CASA index showed high discrimination for mortality with an AUC of 0.91 (sensitivity 98.6%; specificity 69%) and a better performance compared to SOFA (AUC = 0.76), age (AUC = 0.76) and 4C mortality (AUC = 0.82). The cut-off identified (11.994) for CASA index showed a negative predictive value of 99.16% and a positive predictive value of 57.58%. Conclusions: A quick and readily available index has been identified to help clinicians stratify COVID-19 patients according to the appropriate intensity of care and minimize hospital admission to patients at high risk of mortality.

The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots’ capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients’ recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.

One of the current challenges for translational rehabilitation research is to develop the strategies to deliver accurate evaluation, prediction, patient selection, and decision-making in the clinical practice. In this regard, the robot-assisted interventions have gained popularity as they can provide the objective and quantifiable assessment of the motor performance by taking the kinematics parameters into the account. Neurophysiological parameters have also been proposed for this purpose due to the novel advances in the non-invasive signal processing techniques. In addition, other parameters linked to the motor learning and brain plasticity occurring during the rehabilitation have been explored, looking for a more holistic rehabilitation approach. However, the majority of the research done in this area is still exploratory. These parameters have shown the capability to become the “biomarkers” that are defined as the quantifiable indicators of the physiological/pathological processes and the responses to the therapeutical interventions. In this view, they could be finally used for enhancing the robot-assisted treatments. While the research on the biomarkers has been growing in the last years, there is a current need for a better comprehension and quantification of the neuromechanical processes involved in the rehabilitation. In particular, there is a lack of operationalization of the potential neuromechanical biomarkers into the clinical algorithms. In this scenario, a new framework called the “Rehabilomics” has been proposed to account for the rehabilitation research that exploits the biomarkers in its design. This study provides an overview of the state-of-the-art of the biomarkers related to the robotic neurorehabilitation, focusing on the translational studies, and underlying the need to create the comprehensive approaches that have the potential to take the research on the biomarkers into the clinical practice. We then summarize some promising biomarkers that are being under investigation in the current literature and provide some examples of their current and/or potential applications in the neurorehabilitation. Finally, we outline the main challenges and future directions in the field, briefly discussing their potential evolution and prospective.

When thinking about science and scientists, the first names coming to mind belong to men. That is because only a few female scientists are widely recognized for their contributions to the history of science and engineering. The reasons for this trend are well known and yet difficult to eradicate. In 1927 at the famous Solvay conference in Belgium, 29 out of 30 attendees were men. They were all prestigious scientists, such as Albert Einstein and Erwin Schrödinger. The only woman was Marie Curie, who at that time had already won two Nobel prizes. Despite the position of women in the society has changed since 1927, biases and stereotypes still remain and jeopardize a female's chances of becoming a scientist. Even if today most science departments aim at equal opportunities more than one century ago, there is still considerable progress left to make. Indeed, the gender balance is preserved mostly at early stage of career (i.e. among PhDs and young Post Docs) but in most colleges and universities, Principal Investigators are still mostly men.Many highly influential and successful women are contributing to science and in particular to the fields of Neuroengineering and Neurotechnologies, both in the academic and non-academic sector. Yet, female scientists and managers are still underrepresented in various aspects of both academic life (e.g. keynote speakers at conference, directors of research, directors of infrastructures) and industry world (e.g. founders of tech companies, CEOs, top managers). Several initiatives have been recently created to increase the visibility of women; however, gender bias, gender gap and glass ceiling area matter of fact, if concrete actions are not taken by the politician but also by scientists, as a community and as a society. Indeed, inclusion, equal opportunities, promotion of diversity and gender equality, is not only a goal of the United Nations 2030 Agenda, but it is essential milestone enabling the achievement of all 17 Sustainable Development Goals At the 'IEEE Women in Engineering International Leadership Summit' held in Genova, Italy, in 2021, science, mentorship, competitiveness, leadership, innovation, diversity, and parenting have been discussed with leaders in the fields of Neuroengineering and related fields. Rooting on the results of that Summit, this Research Topic aimed at broadening the audience, actively promoting the dissemination of scientific work involving women scientists, mostly in the field of neuroscience, neural engineering, neuroprosthetics, neural and nanotechnology and computational neuroscience. Eight manuscripts were accepted and published within this Research Topic, targeting diverse article types (i.e. 1 Original Research, 1 Brief Research Report, 1 Review, 3 Opinions and 2 Book Reviews), but with a common denominator: promoting research and ideas driven by women.The paper by Soroushmojdehi et al. (https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.977328/full) led by M. Gandolla, proposes a new methodology for decoding the hand movement intention from electromyography (EMG) signals. Since creating a large dataset for a single subject to train deep networks could be very time-consuming, the authors propose (i) a subject-transfer framework, which allows a model to use knowledge learned from other subjects' EMG data; (ii) a task-transfer framework, where the knowledge acquired from classifying basic hand movements is applied to more complex movements that involve combinations of these basic actions. Two Convolutional Neural Networks -based architectures were introduced for hand movement intention detection and a subject-transfer learning approach. Results show that the subject-transfer learning approach increased classification accuracy. Additionally, the task-transfer approach demonstrated that complex hand movements could be classified by leveraging knowledge from simpler movements. Globally, the study demonstrates that transfer learning can improve significantly the performance of EMG-based decoder for neural interface applications.The work by Morelli et al. (https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1158438/full), led by S. Signorini, introduces the TechArm system, a novel technology for visual rehabilitation. The system is capable of providing both uni-and multi-sensory stimuli (audio and tactile) to help visually impaired children to improve their ability to interpret non-visual cues. Participants (lowvision, blind, and sighted children) received auditory, tactile, or combined audio-tactile stimuli by means of four TechArm's units were placed on their forearm and were asked to identify the number of active units. Results showed that tactile stimuli led to the best performance, while auditory accuracy was near chance. The combined audio-tactile condition was more effective than auditory stimuli alone, indicating the benefits of multisensory stimulation. The reported findings support TechArm's potential in developing personalized therapies for the rehabilitation of visually impaired children.Two Opinion papers were presented by the group of F. Tecchio. In the first one, Persichilli and colleagues (https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.913410/full) hypothesize that the triadic principle feedback-synchrony-plasticity (FeeSyCy) governs the adaptive capacity of the body-brain system and underlies the effectiveness of Eye Movement Desensitisation and Reprocessing (EMDR) to fight against major neurological disorders. In the Opinion, the authors discuss about post-traumatic stress disorder (PTSD), affecting twice women than men, as a case study, since it provides an exemplificative case of a dramatic alteration of the adaptive physiological nature of the body-brain system. In the second Opinion paper, Grifoni et al. (https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1393767/full) capitalize on recent findings from relevant pathophysiological contexts, to indicate therapeutic approaches in musicians' dystonia (MD), a task-specific, mostly painless, neurological condition that disrupts musicians' ability to play their instruments. The paper emphasizes the necessity of diverse interventions, from sensorimotor therapies like physiotherapy to approaches targeting the subcortical areas involved in memory, identity, and emotion regulation. The adoption of this comprehensive approach will likely alleviate symptoms, build resilience, and improve quality of life for those affected by this challenging neurological condition.Two Book reviews are also part of the Research Topic. The first one (Armonaite et al. https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.1078376/full) presents and discusses the book by A. Di Leva titled 'The fractal geometry of the brain'. The book offers a comprehensive exploration of fractal geometry in neuroscience, presenting its applications in brain morphology, clinical analysis of neurodegenerative diseases, and computational modeling of brain dynamics, highlighting the brain's complexity through the fractal concept. The second one (Gianni and Tecchio https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.1082143/full) reviews the book by A. Brunoni titled 'Transcranial direct current stimulation in neuropsychiatric disorders. Clinical principles and management', re-issued in 2021 with a new expanded edition.The book describes the mechanisms of action of the main techniques for transcranial electrical stimulation as well as their current and potential applications, providing both perspectives for electroceutical treatments and limitations.The last two papers published in this Research Topic are not directly related to scientific results or scientific discussions led by women. Instead, they focus on important aspects of women's life: working in challenging fields and parenthood.The paper by Jantz et al (https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1104419/full) is a well detailed review of the literature focused on the experiences of women in academic careers in fields closely related to neural engineering. They also reported interviews to women scientists, to identify many of the obstacles women face in the course of their careers and provide recommendations and materials to overcome them.Opinion article by Pedrocchi (https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.853329/full) highlights an underappreciated perspective: parenthood as a professional asset. Instead of viewing parenthood solely through the lens of work-life balance, the author emphasizes its role in developing valuable skills such as maturity, time management, empathy, and leadership-critical in academia and STEM fields. Pedrocchi advocates for workplace policies that integrate work and family life, including flexible work arrangements and mentoring programs. Such measures encourage young professionals to embrace parenthood without fear of career setbacks, fostering a more inclusive and resilient workforce. This perspective challenges traditional narratives, emphasizing the need to recognize parenthood's contributions to both personal and professional growth while promoting a culture that values diverse life experiences. This Research Topic gave all the authors and the Guest Editors the opportunity to start filling that 'gender gap' that is still present in our society and in the STEM areas. The road is still long, but creating awareness and promoting diversity at all professional levels is fundamental to push the boundaries, to overcome stereotypes and constrains and to propose new models for the next generations of scientist approaching STEM, driving them to the unique possible direction for an inclusive and sustainable future.In line with this vision, this research Topic will help in promoting discussion, breaking the stigma and ove

Background Closing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions. Methods We propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively. The new method employed Gaussian interpolation to modulate the intensity profile across a single array of vibration motors (compact feedback) to convey wrist rotation and hand aperture by adjusting the mean and standard deviation of the Gaussian, respectively. Ten able-bodied participants and four transradial amputees performed a target achievement control test by utilizing pattern recognition with compact and conventional vibrotactile feedback to control the Hannes prosthetic hand (test conditions). A second group of ten able-bodied participants performed the same experiment in control conditions with visual and auditory feedback as well as no-feedback. Results Conventional and compact approaches resulted in similar positioning accuracy, time and path efficiency, and total trial time. The comparison with control condition revealed that vibrational feedback was intuitive and useful, but also underlined the power of incidental feedback sources. Notably, amputee participants achieved similar performance to that of able-bodied participants. Conclusions The study therefore shows that the novel feedback strategy conveys useful information about prosthesis movements while reducing the number of motors without compromising performance. This is an important step toward the full integration of such an interface into a prosthesis socket for clinical use.

Recent studies provide novel insights into the meso‐scale organization of the brain, highlighting the co‐occurrence of different structures: classic assortative (modular), disassortative, and core‐periphery. However, the spectral properties of the brain meso‐scale remain mostly unexplored. To fill this knowledge gap, we investigated how the meso‐scale structure is organized across the frequency domain. We analyzed the resting state activity of healthy participants with source‐localized high‐density electroencephalography signals. Then, we inferred the community structure using weighted stochastic block‐model (WSBM) to capture the landscape of meso‐scale structures across the frequency domain. We found that different meso‐scale modalities co‐exist and are diversely organized over the frequency spectrum. Specifically, we found a core‐periphery structure dominance, but we also highlighted a selective increase of disassortativity in the low frequency bands (<8 Hz), and of assortativity in the high frequency band (30–50 Hz). We further described other features of the meso‐scale organization by identifying those brain regions which, at the same time, (a) exhibited the highest degree of assortativity, disassortativity, and core‐peripheriness (i.e., participation) and (b) were consistently assigned to the same community, irrespective from the granularity imposed by WSBM (i.e., granularity‐invariance). In conclusion, we observed that the brain spontaneous activity shows frequency‐specific meso‐scale organization, which may support spatially distributed and local information processing. We analyzed the resting state activity of healthy participants with source‐localized high‐density electroencephalography signals. We inferred the community structure using weighted stochastic block‐model (WSBM) to capture the landscape of meso‐scale structures across the frequency domain. We found that different meso‐scale modalities co‐exist and are diversely organized over the frequency spectrum. PLEASE ADD HERE NEWLY UPLOADED FIGURE #1 (see ATTACHEMENTS)

This work describes a dataset containing high-density EEG (hd-EEG) and surface electromiography (sEMG) to capture neuromechanical responses during a reaching task with and without the assistance of an upper-limb exoskeleton. It was designed to explore electrophysiological biomarkers for assessing assistive technologies. Data were collected from 40 healthy participants performing 10 repetitions of three standardized reaching tasks. A custom-designed touch panel was built to standardize and simulate natural upper-limb movements relevant to daily activities. The dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard, in alignment with FAIR principles. To provide an overview of data quality, we present subject-level analyses of event-related spectral perturbation (ERSP), inter-trial coherence (ITC), and event-related synchronization/desynchronization (ERS/ERD) for EEG, along with time- and frequency- domain decomposition for EMG. Beyond providing a methodology for evaluating assistive technologies, this dataset can be used for biosignal processing research, particularly for artifact removal and denoising techniques. It is also valuable for machine learning-based feature extraction, classification, and studying neuromechanical modulations during goal-oriented movements. Additionally, it can support research on human-robot interaction in non-clinical settings, hybrid brain-computer interfaces (BCIs) for robotic control and biomechanical modeling of upper-limb movements.

Background: In recent years, biomedical devices have proven to be able to target also different neurological disorders. Given the rapid ageing of the population and the increase of invalidating diseases affecting the central nervous system, there is a growing demand for biomedical devices of immediate clinical use. However, to reach useful therapeutic results, these tools need a multidisciplinary approach and a continuous dialogue between neuroscience and engineering, a field that is named neuroengineering. This is because it is fundamental to understand how to read and perturb the neural code in order to produce a significant clinical outcome. Results: In this review, we first highlight the importance of developing novel neurotechnological devices for brain repair and the major challenges expected in the next years. We describe the different types of brain repair strategies being developed in basic and clinical research and provide a brief overview of recent advances in artificial intelligence that have the potential to improve the devices themselves. We conclude by providing our perspective on their implementation to humans and the ethical issues that can arise. Conclusions: Neuroengineering approaches promise to be at the core of future developments for clinical applications in brain repair, where the boundary between biology and artificial intelligence will become increasingly less pronounced.