Reinforcement of Dextran Methacrylate-Based Hydrogel, Semi-IPN, and IPN with Multivalent Crosslinkers.

The need for new biomaterials to meet the needs of advanced healthcare therapies is constantly increasing. Polysaccharide-based matrices are considered extremely promising because of their biocompatibility and soft structure; however, their use is limited by their poor mechanical properties. In this light, a strategy for the reinforcement of dextran-based hydrogels and interpenetrated polymer networks (semi-IPNs and IPNs) is proposed, which will introduce multifunctional crosslinkers that can modify the network crosslinking density.

Towards safer robot-assisted surgery: A markerless augmented reality framework.

Robot-assisted surgery is rapidly developing in the medical field, and the integration of augmented reality shows the potential to improve the operation performance of surgeons by providing more visual information. In this paper, we proposed a markerless augmented reality framework to enhance safety by avoiding intra-operative bleeding, which is a high risk caused by collision between surgical instruments and delicate blood vessels (arteries or veins). Advanced stereo reconstruction and segmentation networks are compared to find the best combination to reconstruct the intra-operative blood vessel in 3D space for registration with the pre-operative model, and the minimum distance detection between the instruments and the blood vessel is implemented.

Spatio-temporal layers based intra-operative stereo depth estimation network via hierarchical prediction and progressive training.

Background And Objective: Safety of robotic surgery can be enhanced through augmented vision or artificial constraints to the robotl motion, and intra-operative depth estimation is the cornerstone of these applications because it provides precise position information of surgical scenes in 3D space. High-quality depth estimation of endoscopic scenes has been a valuable issue, and the development of deep learning provides more possibility and potential to address this issue.

Methods: In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes.

IMU-based classification of resistive exercises for real-time training monitoring on board the international space station with potential telemedicine spin-off.

The microgravity exposure that astronauts undergo during space missions lasting up to 6 months induces biochemical and physiological changes potentially impacting on their health. As a countermeasure, astronauts perform an in-flight training program consisting in different resistive exercises. To train optimally and safely, astronauts need guidance by on-ground specialists via a real-time audio/video system that, however, is subject to a communication delay that increases in proportion to the distance between sender and receiver.

FRSR: Framework for real-time scene reconstruction in robot-assisted minimally invasive surgery.

3D reconstruction of the intra-operative scenes provides precise position information which is the foundation of various safety related applications in robot-assisted surgery, such as augmented reality. Herein, a framework integrated into a known surgical system is proposed to enhance the safety of robotic surgery. In this paper, we present a scene reconstruction framework to restore the 3D information of the surgical site in real time.

Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma.

Purpose: Primary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. Though radiomics-based machine learning (ML) has demonstrated the promising performance in PCNSL, it demands large amounts of manual feature extraction efforts from magnetic resonance images beforehand.

FUN-SIS: A Fully UNsupervised approach for Surgical Instrument Segmentation.

Automatic surgical instrument segmentation of endoscopic images is a crucial building block of many computer-assistance applications for minimally invasive surgery. So far, state-of-the-art approaches completely rely on the availability of a ground-truth supervision signal, obtained via manual annotation, thus expensive to collect at large scale. In this paper, we present FUN-SIS, a Fully-UNsupervised approach for binary Surgical Instrument Segmentation.

Robot-assisted ex vivo neobladder reconstruction: preliminary results of surgical skill evaluation.

Purpose: Advanced developments in the medical field have gradually increased the public demand for surgical skill evaluation. However, this assessment always depends on the direct observation of experienced surgeons, which is time-consuming and variable. The introduction of robot-assisted surgery provides a new possibility for this evaluation paradigm.

Automating Endoscope Motion in Robotic Surgery: A Usability Study on da Vinci-Assisted Neobladder Reconstruction.

Robots for minimally invasive surgery introduce many advantages, but still require the surgeon to alternatively control the surgical instruments and the endoscope. This work aims at providing autonomous navigation of the endoscope during a surgical procedure. The autonomous endoscope motion was based on kinematic tracking of the surgical instruments and integrated with the da Vinci Research Kit.

A Robotic System with EMG-Triggered Functional Eletrical Stimulation for Restoring Arm Functions in Stroke Survivors.

Background: Robotic systems combined with Functional Electrical Stimulation (FES) showed promising results on upper-limb motor recovery after stroke, but adequately-sized randomized controlled trials (RCTs) are still missing.

Objective: To evaluate whether arm training supported by RETRAINER, a passive exoskeleton integrated with electromyograph-triggered functional electrical stimulation, is superior to advanced conventional therapy (ACT) of equal intensity in the recovery of arm functions, dexterity, strength, activities of daily living, and quality of life after stroke.

Methods: A single-blind RCT recruiting 72 patients was conducted.

Acceptance and Usability of Immersive Virtual Reality in Older Adults with Objective and Subjective Cognitive Decline.

Background: Virtual reality (VR) has recently emerged as a promising means for the administration of cognitive training of seniors at risk of dementia. Immersive VR could result in increased engagement and performances; however, its acceptance in older adults with cognitive deficits still has to be assessed.

Objective: To assess acceptance and usability of an immersive VR environment requiring real walking and active participants' interaction.

Dystonia in Angelman syndrome: a common, unrecognized clinical finding.

Introduction: Angelman syndrome (AS) is a neurodevelopmental disorder characterized by cognitive disability, speech impairment, hyperactivity and seizures. Movement disorders have been reported in almost all AS subjects and they are described as "tremulous movements of limbs, unsteadiness, clumsiness or quick, jerky motions". The presence of dystonia has barely been mentioned in subjects with AS and has never been studied in detail.

Functional reconstitution of HBV-specific CD8 T cells by in vitro polyphenol treatment in chronic hepatitis B.

Background & Aims: In chronic HBV infection, mitochondrial functions and proteostasis are dysregulated in exhausted HBV-specific CD8 T cells. To better characterise the potential involvement of deregulated protein degradation mechanisms in T cell exhaustion, we analysed lysosome-mediated autophagy in HBV-specific CD8 T cells. Bioactive compounds able to simultaneously target both mitochondrial functions and proteostasis were tested to identify optimal combination strategies to reconstitute efficient antiviral CD8 T cell responses in patients with chronic HBV infection.

Experimental validation of manipulability optimization control of a 7-DoF serial manipulator for robot-assisted surgery.

Purpose: Both safety and accuracy are of vital importance for surgical operation procedures. An efficient way to avoid the singularity of the surgical robot concerning safety issues is to maximize its manipulability in robot-assisted surgery. The goal of this work was to validate a dynamic neural network optimization method for manipulability optimization control of a 7-degree of freedom (DoF) robot in a surgical operation.

Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results.

In this paper, an improved recurrent neural network (RNN) scheme is proposed to perform the trajectory control of redundant robot manipulators using remote center of motion (RCM) constraints. Firstly, learning by demonstration is implemented to model the surgical operation skills in the Cartesian space. After that, considering the kinematic constraints associated with the optimization control of redundant manipulators, we propose a novel RNN-based approach to facilitate accurate task tracking based on the general quadratic performance index, which includes managing the constraints on RCM joint angle, and joint velocity, simultaneously.

Connectivity measures suggest a sub-cortical generator of myoclonus in Angelman syndrome.

Objective: The clinical and neurophysiological characteristics of myoclonus in Angelman syndrome (AS) have been evaluated in single case or small cohorts, with contrasting results. We evaluated the features of myoclonus in a wide cohort of AS patients.

Methods: We performed polygraphic EEG-EMG recording in 24 patients with genetically confirmed AS and myoclonus.

A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone.

As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans' daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive growth of wearable and mobile devices. They provide the most availability of human activity data (big data).

Towards Model-Free Tool Dynamic Identification and Calibration Using Multi-Layer Neural Network.

In robot control with physical interaction, like robot-assisted surgery and bilateral teleoperation, the availability of reliable interaction force information has proved to be capable of increasing the control precision and of dealing with the surrounding complex environments. Usually, force sensors are mounted between the end effector of the robot manipulator and the tool for measuring the interaction forces on the tooltip. In this case, the force acquired from the force sensor includes not only the interaction force but also the gravity force of the tool.

Design and Integration of Electrical Bio-impedance Sensing in Surgical Robotic Tools for Tissue Identification and Display.

The integration of intra-operative sensors into surgical robots is a hot research topic since this can significantly facilitate complex surgical procedures by enhancing surgical awareness with real-time tissue information. However, currently available intra-operative sensing technologies are mainly based on image processing and force feedback, which normally require heavy computation or complicated hardware modifications of existing surgical tools. This paper presents the design and integration of electrical bio-impedance sensing into a commercial surgical robot tool, leading to the creation of a novel smart instrument that allows the identification of tissues by simply touching them.

A Hybrid Robotic System for Arm Training of Stroke Survivors: Concept and First Evaluation.

Objective: To develop and evaluate a hybrid robotic system for arm recovery after stroke, combining ElectroMyoGraphic (EMG)-triggered functional electrical stimulation (FES) with a passive exoskeleton for upper limb suspension.

Methods: The system was used in a structured exercise program resembling activities of daily life. Exercises execution was continuously controlled using angle sensor data and radio-frequency identification technology.

Virtual reality-based wheelchair simulators: A scoping review.

Sense of presence (SoP) has recently emerged as one of the key elements promoting the effectiveness of virtual reality-based training programs. In the context of wheelchair simulators (WSs), the effectiveness of the simulation has been sought using different perception and interaction devices, providing the end-users with different levels of SoP. We performed a scoping review searching scientific and grey literature databases with the aim of assessing the extent of published research dealing with SoP and effectiveness of WSs.

FCNN-based axon segmentation for convection-enhanced delivery optimization.

Purpose: Glioblastoma multiforme treatment is a challenging task in clinical oncology. Convection- enhanced delivery (CED) is showing encouraging but still suboptimal results due to drug leakages. Numerical models can predict drug distribution within the brain, but require retrieving brain physical properties, such as the axon diameter distribution (ADD), through axon architecture analysis.

Analysis for the design of a novel integrated framework for the return to work of wheelchair users.

Background: Return to work represents an important milestone for workers who were injured during a workplace accident, especially if the injury results in needing a wheelchair for locomotion.

Objective: The aim of the study was to design a framework for training novice wheelchair users in regaining autonomy in activities of daily living and in the workplace and for providing medical personnel with objective data on users' health and work-related capabilities.

Methods: The framework design was accomplished following the "Usability Engineering Life Cycle" model.

"Deep-Onto" network for surgical workflow and context recognition.

Purpose: Surgical workflow recognition and context-aware systems could allow better decision making and surgical planning by providing the focused information, which may eventually enhance surgical outcomes. While current developments in computer-assisted surgical systems are mostly focused on recognizing surgical phases, they lack recognition of surgical workflow sequence and other contextual element, e.g.

Need for a standardized technique of nap EEG recordings: results of a national online survey in Italy.

The aim of this study is to report current clinical practice for sleep induction in Italian epilepsy centers. We administered an online-structured survey between March and November 2017 and collected data from pediatric and adult neurophysiologists belonging to 73 epilepsy centers. The preferred time for EEG recording is variable, depending on daily schedule of each laboratory.