Ireland's central source for Open Access health research
Lenus, the Irish Health Research repository is the leading source for Irish research in health and social care. The Lenus collections include peer reviewed journal articles, grey literature, dissertations, reports and conference presentations. Lenus contains the publications of the Irish Health Service Executive (HSE) and the collected research output of over 130 health organisations past and present are all freely accessible.
If you are an Irish researcher or have conducted research in an Irish institution or health organisation, you can add your published research to Lenus. Submitted articles must be available in Open Access format or the publisher's policy must permit author self archiving. Advice on Open Access publishing and publishers' policies is available on the 'Open Access Publishing Guide' and 'Publishers' policies' pages available on the left-hand menu.
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HSE Open Access Research Awards 2020
Despite the obstacles presented by the coronavirus, the HSE Open Access Awards went ahead as usual this year, in an all-virtual form. The usual range of subject categories was replaced by just one: Covid-19, and the presentation ceremony took place on Friday 11th December 2020.
The standard of entries was excellent, and external judge Professor Jonathan Drennan said it was extremely hard to choose between them. “It was an extremely difficult decision – they were extremely high quality – but it’s an enjoyable process. It was great to see the quality and standards reviewed across all the applications.”
The winners of the HSE Open Access Awards 2020 are:
Dale Francis Whelehan and colleagues: COVID-19 and surgery: A thematic analysis of unintended consequences on performance, practice and surgical training.
Dónal Ó Mathúna and colleagues: Clinical, laboratory and radiological characteristics and outcomes of novel coronavirus (SARS-CoV-2) infection in humans: A systematic review and series of meta-analyses.
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Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson's Disease.This study presents a computational model of closed-loop control of deep brain stimulation (DBS) for Parkinson's disease (PD) to investigate clinically viable control schemes for suppressing pathological beta-band activity. Closed-loop DBS for PD has shown promising results in preliminary clinical studies and offers the potential to achieve better control of patient symptoms and side effects with lower power consumption than conventional open-loop DBS. However, extensive testing of algorithms in patients is difficult. The model presented provides a means to explore a range of control algorithms in silico and optimize control parameters before preclinical testing. The model incorporates (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode and (iv) temporal variation of network beta-band activity within the thalamo-cortico-basal ganglia loop. The performance of on-off and dual-threshold controllers for suppressing beta-band activity by modulating the DBS amplitude were first verified, showing levels of beta suppression and reductions in power consumption comparable with previous clinical studies. Proportional (P) and proportional-integral (PI) closed-loop controllers for amplitude and frequency modulation were then investigated. A simple tuning rule was derived for selecting effective PI controller parameters to target long duration beta bursts while respecting clinical constraints that limit the rate of change of stimulation parameters. Of the controllers tested, PI controllers displayed superior performance for regulating network beta-band activity whilst accounting for clinical considerations. Proportional controllers resulted in undesirable rapid fluctuations of the DBS parameters which may exceed clinically tolerable rate limits. Overall, the PI controller for modulating DBS frequency performed best, reducing the mean error by 83% compared to DBS off and the mean power consumed to 25% of that utilized by open-loop DBS. The network model presented captures sufficient physiological detail to act as a surrogate for preclinical testing of closed-loop DBS algorithms using a clinically accessible biomarker, providing a first step for deriving and testing novel, clinically suitable closed-loop DBS controllers.