Stroke secondary prevention: everyone's business

Stroke secondary prevention is everyone’s business and requires cohesive working across the multiprofessional team and beyond [...

Chemotherapy induced peripheral neuropathy: the modified total neuropathy score in clinical practice.

BACKGROUND: Chemotherapy-induced peripheral neuropathy (CIPN) is a common, potentially reversible side effect of some chemotherapeutic agents. CIPN is associated with decreased balance, function and quality of life (QoL). This association has to date been under-investigated. AIMS: To profile patients presenting with CIPN using the modified Total Neuropathy Score (mTNS) in this cross-sectional study and to examine the relationship between CIPN (measured by mTNS) and indices of balance, quality of life (QoL) and function. METHODS: Patients receiving neurotoxic chemotherapy regimens were identified using hospital databases. Those who did not have a pre-existing neuropathy were invited to complete mTNS, Berg Balance Scale (BBS), timed up and go (TUG), and FACT-G QoL questionnaire. mTNS scores were profiled and also correlated with BBS, TUG and FACT-G using Spearmans correlation coefficient. RESULTS: A total of 29 patients undergoing neurotoxic chemotherapy regimens were tested. The patients mTNS scores ranged between 1 and 12 (median = 5), indicating that all patients had clinical evidence of neuropathy on mTNS. No significant correlations were found between mTNS and BERG (r = -0.29), TUG (r = 0.14), or FACT-G (r = 0.05). CONCLUSIONS: This study found a high prevalence of CIPN in patients treated with neurotoxic chemotherapy regimens. The mTNS provided a clinically applicable, sensitive screening tool for CIPN which could prove useful in clinical practice. mTNS did not correlate with BBS, TUG or FACT-G in this sample, possibly due to relatively mild levels of CIPN and consequent subtle impairments which were not adequately captured by gross functional assessments

Exoskeleton Training Modulates Complexity in Movement Patterns and Cortical Activity in Able-Bodied Volunteers

Robot-aided gait training (RAGT) plays a crucial role in providing high-dose and high-intensity task-oriented physical therapy. The human-robot interaction during RAGT remains technically challenging. To achieve this aim, it is necessary to quantify how RAGT impacts brain activity and motor learning. This work quantifies the neuromuscular effect induced by a single RAGT session in healthy middle-aged individuals. Electromyographic (EMG) and motion (IMU) data were recorded and processed during walking trials before and after RAGT. Electroencephalographic (EEG) data were recorded during rest before and after the entire walking session. Linear and nonlinear analyses detected changes in the walking pattern, paralleled by a modulation of cortical activity in the motor, attentive, and visual cortices immediately after RAGT. Increases in alpha and beta EEG spectral power and pattern regularity of the EEG match the increased regularity of body oscillations in the frontal plane, and the loss of alternating muscle activation during the gait cycle, when walking after a RAGT session. These preliminary results improve the understanding of human-machine interaction mechanisms and motor learning and may contribute to more efficient exoskeleton development for assisted walking

Effect of lower limb exoskeleton on the modulation of neural activity and gait classification

: Neurorehabilitation with robotic devices requires a paradigm shift to enhance human-robot interaction. The coupling of robot assisted gait training (RAGT) with a brain-machine interface (BMI) represents an important step in this direction but requires better elucidation of the effect of RAGT on the user's neural modulation. Here, we investigated how different exoskeleton walking modes modify brain and muscular activity during exoskeleton assisted gait. We recorded electroencephalographic (EEG) and electromyographic (EMG) activity from ten able-bodied volunteers walking with an exoskeleton with three modes of user assistance (i.e., transparent, adaptive and full assistance) and during free overground gait. Results identified that exoskeleton walking (irrespective of the exoskeleton mode) induces a stronger modulation of central mid-line mu (8-13 Hz) and low-beta (14-20 Hz) rhythms compared to free overground walking. These modifications are accompanied by a significant re-organization of the EMG patterns in exoskeleton walking. On the other hand, we observed no significant differences in neural activity during exoskeleton walking with the different assistance levels. We subsequently implemented four gait classifiers based on deep neural networks trained on the EEG data during the different walking conditions. Our hypothesis was that exoskeleton modes could impact the creation of a BMI-driven RAGT. We demonstrated that all classifiers achieved an average accuracy of 84.13 ± 3.49% in classifying swing and stance phases on their respective datasets. In addition, we demonstrated that the classifier trained on the transparent mode exoskeleton data can classify gait phases during adaptive and full modes with an accuracy of 78.3 ± 4.8%, while the classifier trained on free overground walking data fails to classify the gait during exoskeleton walking (accuracy of 59.4 ± 11.8%). These findings provide important insights into the effect of robotic training on neural activity and contribute to the advancement of BMI technology for improving robotic gait rehabilitation therapy

A Systematic Review Establishing the Current State-of-the-Art, the Limitations, and the DESIRED Checklist in Studies of Direct Neural Interfacing With Robotic Gait Devices in Stroke Rehabilitation

Background: Stroke is a disease with a high associated disability burden. Robotic-assisted gait training offers an opportunity for the practice intensity levels associated with good functional walking outcomes in this population. Neural interfacing technology, electroencephalography (EEG), or electromyography (EMG) can offer new strategies for robotic gait re-education after a stroke by promoting more active engagement in movement intent and/or neurophysiological feedback. Objectives: This study identifies the current state-of-the-art and the limitations in direct neural interfacing with robotic gait devices in stroke rehabilitation. Methods: A pre-registered systematic review was conducted using standardized search operators that included the presence of stroke and robotic gait training and neural biosignals (EMG and/or EEG) and was not limited by study type. Results: From a total of 8,899 papers identified, 13 articles were considered for the final selection. Only five of the 13 studies received a strong or moderate quality rating as a clinical study. Three studies recorded EEG activity during robotic gait, two of which used EEG for BCI purposes. While demonstrating utility for decoding kinematic and EMG-related gait data, no EEG study has been identified to close the loop between robot and human. Twelve of the studies recorded EMG activity during or after robotic walking, primarily as an outcome measure. One study used multisource information fusion from EMG, joint angle, and force to modify robotic commands in real time, with higher error rates observed during active movement. A novel study identified used EMG data during robotic gait to derive the optimal, individualized robot-driven step trajectory. Conclusions: Wide heterogeneity in the reporting and the purpose of neurobiosignal use during robotic gait training after a stroke exists. Neural interfacing with robotic gait after a stroke demonstrates promise as a future field of study. However, as a nascent area, direct neural interfacing with robotic gait after a stroke would benefit from a more standardized protocol for biosignal collection and processing and for robotic deployment. Appropriate reporting for clinical studies of this nature is also required with respect to the study type and the participants' characteristics

Stroke secondary prevention, a non-surgical and non-pharmacological consensus definition : results of a Delphi study

OBJECTIVE: Evidence supporting lifestyle modification in vascular risk reduction is limited, drawn largely from primary prevention studies. To advance the evidence base for non-pharmacological and non-surgical stroke secondary prevention (SSP), empirical research is needed, informed by a consensus-derived definition of SSP. To date, no such definition has been published. We used Delphi methods to generate an evidence-based definition of non-pharmacological and non-surgical SSP. RESULTS: The 16 participants were members of INSsPiRE (International Network of Stroke Secondary Prevention Researchers), a multidisciplinary group of trialists, academics and clinicians. The Elicitation stage identified 49 key elements, grouped into 3 overarching domains: Risk factors, Education, and Theory before being subjected to iterative stages of elicitation, ranking, discussion, and anonymous voting. In the Action stage, following an experience-based engagement with key stakeholders, a consensus-derived definition, complementing current pharmacological and surgical SSP pathways, was finalised: Non-pharmacological and non-surgical stroke secondary prevention supports and improves long-term health and well-being in everyday life and reduces the risk of another stroke, by drawing from a spectrum of theoretically informed interventions and educational strategies. Interventions to self-manage modifiable lifestyle risk factors are contextualized and individualized to the capacities, needs, and personally meaningful priorities of individuals with stroke and their families

Precision abundance analysis of bright HII galaxies

We present high signal-to-noise spectrophotometric observations of seven luminous HII galaxies. The observations have been made with the use of a double-arm spectrograph which provides spectra with a wide wavelength coverage, from 3400 to 10400\AA free of second order effects, of exactly the same region of a given galaxy. These observations are analysed applying a methodology designed to obtain accurate elemental abundances of oxygen, sulphur, nitrogen, neon, argon and iron in the ionized gas. Four electron temperatures and one electron density are derived from the observed forbidden line ratios using the five-level atom approximation. For our best objects errors of 1% in t_e([OIII]), 3% in t_e([OII]) and 5% in t_e([SIII]) are achieved with a resulting accuracy of 7% in total oxygen abundances, O/H. The ionisation structure of the nebulae can be mapped by the theoretical oxygen and sulphur ionic ratios, on the one side, and the corresponding observed emission line ratios, on the other -- the \eta and \eta' plots --. The combination of both is shown to provide a means to test photo-ionisation model sequences currently applied to derive elemental abundances in HII galaxies.Comment: 24 pages, 8 figures, accepted by MNRA

Abundance analysis of prime B-type targets for asteroseismology I. Nitrogen excess in slowly-rotating beta Cephei stars

We present the results of a detailed NLTE abundance study of nine beta Cephei stars, all of them being prime targets for theoretical modelling: gamma Peg, delta Cet, nu Eri, beta CMa, xi1 CMa, V836 Cen, V2052 Oph, beta Cep and DD (12) Lac. The following chemical elements are considered: He, C, N, O, Mg, Al, Si, S and Fe. Our abundance analysis is based on a large number of time-resolved, high-resolution optical spectra covering in most cases the entire oscillation cycle of the stars. Nitrogen is found to be enhanced by up to 0.6 dex in four stars, three of which have severe constraints on their equatorial rotational velocity, \Omega R, from seismic or line-profile variation studies: beta Cep (\Omega R~26 km/s), V2052 Oph (\Omega R~56 km/s), delta Cet (\Omega R < 28 km/s) and xi1 CMa (\Omega R sin i < 10 km/s). The existence of core-processed material at the surface of such largely unevolved, slowly-rotating objects is not predicted by current evolutionary models including rotation. We draw attention to the fact that three stars in this subsample have a detected magnetic field and briefly discuss recent theoretical work pointing to the occurrence of diffusion effects in beta Cephei stars possibly capable of altering the nitrogen surface abundance. On the other hand, the abundances of all the other chemical elements considered are, within the errors, indistinguishable from the values found for OB dwarfs in the solar neighbourhood. Despite the mild nitrogen excess observed in some objects, we thus find no evidence for a significantly higher photospheric metal content in the studied beta Cephei stars compared to non-pulsating B-type stars of similar characteristics.Comment: Accepted for publication in A&A, 21 pages, 7 figure

A protocol to evaluate the impact of embedding Public and Patient Involvement in a structured PhD program for stroke care

BackgroundEmbedding Public and Patient Involvement (PPI) in postgraduate research has been recognized as an important component of post-graduate training, providing research scholars with an awareness and a skillset in an area which prepares them for future roles as healthcare researchers. Improving Pathways for Acute STroke And Rehabilitation (iPASTAR) is a structured PhD training program [Collaborative Doctoral Award (CDA)] which aims to design a person-centered stroke pathway throughout the trajectory of stroke care, to optimize post-stroke health and wellbeing. PPI is embedded at all stages.PurposeThe iPASTAR research programme was strongly informed by a round-table PPI consultation process with individuals who experienced stroke and who provided broad representation across ages, gender, geographical locations (urban and rural) and the PhD themed areas of acute care, early supported discharge and lifestyle-based interventions after stroke. Four PhD scholars taking part in the CDA-iPASTAR now work collaboratively with four stroke champions, supported by a wider PPI advisory panel.MethodsThis study will evaluate the process and impact of embedding PPI during a PhD program. We will conduct a longitudinal mixed-methods evaluation, conducting focus groups at 24, 36, and 48 months to explore the experiences of the key stakeholders involved. The participants will include PhD scholars, PPI partners (PPI Advisory Group and PPI Champions), PhD supervisors and a PPI manager. An independent researcher will conduct the evaluation. We will include focus groups, individual interviews and participant reflections. Qualitative data will be analyzed using thematic and content analysis, quantitative data will be analyzed using descriptive statistics.DiscussionPPI and patient voice initiatives bring together researchers, family, and people with health care issues into meaningful dialogue and allow the development of a patient-voice learning network. Embedding PPI training within a PhD program can build meaningful capacity in PPI partnerships in stroke research