A neural tracking and motor control approach to improve rehabilitation of upper limb movements

<p>Abstract</p> <p>Background</p> <p>Restoration of upper limb movements in subjects recovering from stroke is an essential keystone in rehabilitative practices. Rehabilitation of arm movements, in fact, is usually a far more difficult one as compared to that of lower extremities. For these reasons, researchers are developing new methods and technologies so that the rehabilitative process could be more accurate, rapid and easily accepted by the patient. This paper introduces the proof of concept for a new non-invasive FES-assisted rehabilitation system for the upper limb, called smartFES (sFES), where the electrical stimulation is controlled by a biologically inspired neural inverse dynamics model, fed by the kinematic information associated with the execution of a planar goal-oriented movement. More specifically, this work details two steps of the proposed system: an <it>ad hoc </it>markerless motion analysis algorithm for the estimation of kinematics, and a neural controller that drives a synthetic arm. The vision of the entire system is to acquire kinematics from the analysis of video sequences during planar arm movements and to use it together with a neural inverse dynamics model able to provide the patient with the electrical stimulation patterns needed to perform the movement with the assisted limb.</p> <p>Methods</p> <p>The markerless motion tracking system aims at localizing and monitoring the arm movement by tracking its silhouette. It uses a specifically designed motion estimation method, that we named Neural Snakes, which predicts the arm contour deformation as a first step for a silhouette extraction algorithm. The starting and ending points of the arm movement feed an Artificial Neural Controller, enclosing the muscular Hill's model, which solves the inverse dynamics to obtain the FES patterns needed to move a simulated arm from the starting point to the desired point. Both position error with respect to the requested arm trajectory and comparison between curvature factors have been calculated in order to determine the accuracy of the system.</p> <p>Results</p> <p>The proposed method has been tested on real data acquired during the execution of planar goal-oriented arm movements. Main results concern the capability of the system to accurately recreate the movement task by providing a synthetic arm model with the stimulation patterns estimated by the inverse dynamics model. In the simulation of movements with a length of ± 20 cm, the model has shown an unbiased angular error, and a mean (absolute) position error of about 1.5 cm, thus confirming the ability of the system to reliably drive the model to the desired targets. Moreover, the curvature factors of the factual human movements and of the reconstructed ones are similar, thus encouraging future developments of the system in terms of reproducibility of the desired movements.</p> <p>Conclusion</p> <p>A novel FES-assisted rehabilitation system for the upper limb is presented and two parts of it have been designed and tested. The system includes a markerless motion estimation algorithm, and a biologically inspired neural controller that drives a biomechanical arm model and provides the stimulation patterns that, in a future development, could be used to drive a smart Functional Electrical Stimulation system (sFES). The system is envisioned to help in the rehabilitation of post stroke hemiparetic patients, by assisting the movement of the paretic upper limb, once trained with a set of movements performed by the therapist or in virtual reality. Future work will include the application and testing of the stimulation patterns in real conditions.</p

Clinical features and comorbidity pattern of HCV infected migrants compared to native patients in care in Italy: A real-life evaluation of the PITER cohort

Background: Direct-acting antivirals are highly effective for the treatment of hepatitis C virus (HCV) infection, regardless race/ethnicity. We aimed to evaluate demographic, virological and clinical data of HCV-infected migrants vs. natives consecutively enrolled in the PITER cohort. Methods: Migrants were defined by country of birth and nationality that was different from Italy. Mann-Whitney U test, Chi-squared test and multiple logistic regression were used. Results: Of 10,669 enrolled patients, 301 (2.8%) were migrants: median age 47 vs. 62 years, (p &lt; 0.001), females 56.5% vs. 45.3%, (p &lt; 0.001), HBsAg positivity 3.8% vs. 1.4%, (p &lt; 0.05). Genotype 1b was prevalent in both groups, whereas genotype 4 was more prevalent in migrants (p &lt; 0.05). Liver disease severity and sustained virologic response (SVR) were similar. A higher prevalence of comorbidities was reported for natives compared to migrants (p &lt; 0.05). Liver disease progression cofactors (HBsAg, HIV coinfection, alcohol abuse, potential metabolic syndrome) were present in 39.1% and 47.1% (p &gt; 0.05) of migrants and natives who eradicated HCV, respectively. Conclusion: Compared to natives, HCV-infected migrants in care have different demographics, HCV genotypes, viral coinfections and comorbidities and similar disease severity, SVR and cofactors for disease progression after HCV eradication. A periodic clinical assessment after HCV eradication in Italians and migrants with cofactors for disease progression is warranted

Economic consequences of investing in anti-HCV antiviral treatment from the Italian NHS perspective : a real-world-based analysis of PITER data

OBJECTIVE: We estimated the cost consequence of Italian National Health System (NHS) investment in direct-acting antiviral (DAA) therapy according to hepatitis C virus (HCV) treatment access policies in Italy. METHODS: A multistate, 20-year time horizon Markov model of HCV liver disease progression was developed. Fibrosis stage, age and genotype distributions were derived from the Italian Platform for the Study of Viral Hepatitis Therapies (PITER) cohort. The treatment efficacy, disease progression probabilities and direct costs in each health state were obtained from the literature. The break-even point in time (BPT) was defined as the period of time required for the cumulative costs saved to recover the Italian NHS investment in DAA treatment. Three different PITER enrolment periods, which covered the full DAA access evolution in Italy, were considered. RESULTS: The disease stages of 2657 patients who consecutively underwent DAA therapy from January 2015 to December 2017 at 30 PITER clinical centres were standardized for 1000 patients. The investment in DAAs was considered to equal €25 million, €15 million, and €9 million in 2015, 2016, and 2017, respectively. For patients treated in 2015, the BPT was not achieved, because of the disease severity of the treated patients and high DAA prices. For 2016 and 2017, the estimated BPTs were 6.6 and 6.2 years, respectively. The total cost savings after 20 years were €50.13 and €55.50 million for 1000 patients treated in 2016 and 2017, respectively. CONCLUSIONS: This study may be a useful tool for public decision makers to understand how HCV clinical and epidemiological profiles influence the economic burden of HCV

L'Italia come modello per l'Europa e per il mondo nelle politiche sanitarie per il trattamento dell'epatite cronica da HCV

The World Health Organization foresees the elimination of HCV infection by 2030. In light of this and the curre nt, nearly worldwide, restriction in direct-acting agents (DAA) accessibility due to their high price, we aimed to evaluate the cost-effectiveness of two alternative DAA treatment policies: Policy 1 (universal): treat all patients, regardless of the fibrosis stage; Policy 2 (prioritized): treat only priori tized patients and delay treatment of the remaining patients until reaching stage F3. T he model was based on patient’s data from the PITER cohort. We demonstrated that extending HC V treatment of patients in any fibrosis stage improves health outcomes and is cost-effective

A multimodal cell census and atlas of the mammalian primary motor cortex

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties

A bio-inspired controller of an upper arm model in a perturbed environment

In humans, multijoint tasks are executed through the integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to gain knowledge on the mechanisms sub-serving these three aspects of motor control. In this general context, artificial neural networks represent a means to represent and interpret the movement of upper limb in normal and altered conditions. In the present work a controller of an upper human arm model based on an artificial neural network is being exposed to different conditions simulate altered force environment, to give insights on the adaptation ability of the human arm to environmental modifications such as the insertion of different force fields acting on the end-effector