Design of a Shaping System for Stereolithography with High Solid Loading Ceramic Suspensions

3D Printing and Additive Manufacturing , Vol. 0, No. 0.


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Nicotine and exercise performance: another tool in the arsenal or curse for anti-doping?

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Cost-Effectiveness of a Behavioral Psychosocial Treatment Integrated Across Home and School for Pediatric ADHD-Inattentive Type

Abstract

We conducted a cost-effectiveness analysis (CEA) of two behavioral psychosocial interventions for children with ADHD-inattentive type: Child Life and Attention Skills (CLAS) program and parent-focused treatment (PFT) compared to community-based treatment as usual (TAU). The CEA evaluated cost per ADHD case resolved measured by parent and teacher reports of ADHD inattentive symptoms. Total cost per patient for CLAS, PFT, and TAU were $1559, $710, and $0. CLAS, the costliest treatment, was more effective than PFT and TAU. The incremental cost-effectiveness ratios (ICER) per disordered case resolved are: $3997 for CLAS versus TAU, $3227 for PFT versus TAU, and $4994 for CLAS versus PFT. PFT is the more cost-effective option based on initial CEA. However, CLAS may be comparably cost-effective by streamlining the model, which resulted in an ICER of $29 compared to PFT. Notably, cost for CLAS is substantially below the annual cost for unresolved ADHD.

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Gene expression: Developmental enhancers in action

Gene expression: Developmental enhancers in action

Gene expression: Developmental enhancers in action, Published online: 26 February 2018; doi:10.1038/nrg.2018.13

Gene expression: Developmental enhancers in action

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Integrative omics for health and disease

Integrative omics for health and disease

Integrative omics for health and disease, Published online: 26 February 2018; doi:10.1038/nrg.2018.4

This article discusses how integrating different omics data types — such as DNA sequencing, transcriptomics and metabolomics — can provide a rich view of healthy and disease states, including novel clinical diagnoses. The authors discuss the value of the different data types, as well as strategies, considerations and challenges for multi-omic integration in various disease contexts.

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Stretching the limits: from homeostasis to stem cell plasticity in wound healing and cancer

Stretching the limits: from homeostasis to stem cell plasticity in wound healing and cancer

Stretching the limits: from homeostasis to stem cell plasticity in wound healing and cancer, Published online: 26 February 2018; doi:10.1038/nrg.2018.9

Adult stem cells are essential for the maintenance of tissue homeostasis and wound repair, but cancer can hijack their tissue regenerative functions to promote malignancy. Ge and Fuchs review recent insights into the determinants and general principles underlying stem cell plasticity under homeostasis, stress and cancer.

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Neural Decoding of Robot-Assisted Gait during Rehabilitation after Stroke

AbstractObjectiveAdvancements in robot-assisted gait rehabilitation and brain-machine interfaces (BMI) may enhance stroke physiotherapy by engaging patients while providing information about robot-induced cortical adaptations. We investigate the feasibility of decoding walking from brain activity in stroke survivors during therapy using a powered exoskeleton integrated with an electroencephalography (EEG)-based BMI.DesignThe H2 powered exoskeleton was designed for overground gait training with actuated hip, knee and ankle joints. It was integrated with active-electrode EEG and evaluated in hemiparetic stroke survivors over 12 sessions/4 weeks. A continuous-time Kalman decoder operating on delta-band EEG was designed to estimate gait kinematics.ResultsFive chronic stroke patients completed the study with improvements in walking distance and speed training over 4 weeks, correlating with increased offline decoding accuracy. Accuracies of predicted joint angles improved with session and gait speed, suggesting an improved neural representation for gait, and the feasibility to design an EEG-based BMI to monitor brain activity or control a rehabilitative exoskeleton.ConclusionThe Kalman decoder showed increased accuracies as the longitudinal training intervention progressed in the stroke participants. These results demonstrate the feasibility of studying changes in patterns of neuroelectric cortical activity during post-stroke rehabilitation and represent the first step in developing a BMI for controlling powered exoskeletons. Objective Advancements in robot-assisted gait rehabilitation and brain-machine interfaces (BMI) may enhance stroke physiotherapy by engaging patients while providing information about robot-induced cortical adaptations. We investigate the feasibility of decoding walking from brain activity in stroke survivors during therapy using a powered exoskeleton integrated with an electroencephalography (EEG)-based BMI. Design The H2 powered exoskeleton was designed for overground gait training with actuated hip, knee and ankle joints. It was integrated with active-electrode EEG and evaluated in hemiparetic stroke survivors over 12 sessions/4 weeks. A continuous-time Kalman decoder operating on delta-band EEG was designed to estimate gait kinematics. Results Five chronic stroke patients completed the study with improvements in walking distance and speed training over 4 weeks, correlating with increased offline decoding accuracy. Accuracies of predicted joint angles improved with session and gait speed, suggesting an improved neural representation for gait, and the feasibility to design an EEG-based BMI to monitor brain activity or control a rehabilitative exoskeleton. Conclusion The Kalman decoder showed increased accuracies as the longitudinal training intervention progressed in the stroke participants. These results demonstrate the feasibility of studying changes in patterns of neuroelectric cortical activity during post-stroke rehabilitation and represent the first step in developing a BMI for controlling powered exoskeletons. Equal contributions, Magdo Bortole and Fangshi Zhu. Correspondence: Jose L Contreras- Vidal, Noninvasive Brain-Machine Interface Systems Laboratory, Department of Electrical and Computer Engineering, University of Houston, N308 Engineering Building I, 77204-4005 Houston, USA. School of Engineering, Tecnologico de Monterrey, Monterrey, Mexico. kcnathan@uh.edu Competing interests: The authors declare that they have no competing interests. Funding: This work has been partially supported by the Noninvasive Brain-Machine Interface Lab at the University of Houston, the Monterrey Institute of Technology, and the HYPER Project (Hybrid Neuroprosthetic and Neurorobotic Devices for Functional Compensation and Rehabilitation of Motor Disorders). Ministerio de Ciencia y Innovación, Spain (CSD2009 - 00067 CONSOLIDER INGENIO 2010). Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.

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