Experimental measurement and numerical modelling of dye washout for investigation of blood residence time in ventricular assist devices

Ventricular assist devices have become the standard therapy for end-stage heart failure. However, their use is still associated with severe adverse events related to the damage done to the blood by fluid dynamic stresses. This damage relates to both the stress magnitude and the length of time the blood is exposed to that stress. We created a dye washout technique which combines experimental and numerical approaches to measure the washout times of ventricular assist devices. The technique was used to investigate washout characteristics of three commercially available and clinically used ventricular assist devices: the CentriMag, HVAD and HeartMate II. The time taken to reach 5% dye concentration at the outlet (T05) was used as an indicator of the total residence time. At a typical level of cardiac support, 5 L/min and 100 mmHg, T05 was 0.93, 0.28 and 0.16 s for CentriMag, HVAD and HeartMate II, respectively, and increased to 5.06, 1.64 and 0.96 s for reduced cardiac support of 1 L/min. Regional variations in washout characteristics are described in this article. While the volume of the flow domain plays a large role in the differences in T05 between the ventricular assist devices, after standardising for ventricular assist device volume, the secondary flow path was found to increase T05 by 35%. The results explain quantitatively, for the first time, why the CentriMag, which exerts low shear stress magnitude, has still been found to cause acquired von Willebrand Syndrome in patients

Automatic Setting Procedure for Exoskeleton-Assisted Overground Gait: Proof of Concept on Stroke Population

Stroke-related locomotor impairments are often associated with abnormal timing and intensity of recruitment of the affected and non-affected lower limb muscles. Restoring the proper lower limbs muscles activation is a key factor to facilitate recovery of gait capacity and performance, and to reduce maladaptive plasticity. Ekso is a wearable powered exoskeleton robot able to support over-ground gait training. The user controls the exoskeleton by triggering each single step during the gait cycle. The fine-tuning of the exoskeleton control system is crucial-it is set according to the residual functional abilities of the patient, and it needs to ensure lower limbs powered gait to be the most physiological as possible. This work focuses on the definition of an automatic calibration procedure able to detect the best Ekso setting for each patient. EMG activity has been recorded from Tibialis Anterior, Soleus, Rectus Femoris, and Semitendinosus muscles in a group of 7 healthy controls and 13 neurological patients. EMG signals have been processed so to obtain muscles activation patterns. The mean muscular activation pattern derived from the controls cohort has been set as reference. The developed automatic calibration procedure requires the patient to perform overground walking trials supported by the exoskeleton while changing parameters setting. The Gait Metric index is calculated for each trial, where the closer the performance is to the normative muscular activation pattern, in terms of both relative amplitude and timing, the higher the Gait Metric index is. The trial with the best Gait Metric index corresponds to the best parameters set. It has to be noted that the automatic computational calibration procedure is based on the same number of overground walking trials, and the same experimental set-up as in the current manual calibration procedure. The proposed approach allows supporting the rehabilitation team in the setting procedure. It has been demonstrated to be robust, and to be in agreement with the current gold standard (i.e., manual calibration performed by an expert engineer). The use of a graphical user interface is a promising tool for the effective use of an automatic procedure in a clinical context

A biofeedback cycling training to improve locomotion: a case series study based on gait pattern classification of 153 chronic stroke patients

<p>Abstract</p> <p>Background</p> <p>The restoration of walking ability is the main goal of post-stroke lower limb rehabilitation and different studies suggest that pedaling may have a positive effect on locomotion. The aim of this study was to explore the feasibility of a biofeedback pedaling treatment and its effects on cycling and walking ability in chronic stroke patients. A case series study was designed and participants were recruited based on a gait pattern classification of a population of 153 chronic stroke patients.</p> <p>Methods</p> <p>In order to optimize participants selection, a k-means cluster analysis was performed to subgroup homogenous gait patterns in terms of gait speed and symmetry.</p> <p>The training consisted of a 2-week treatment of 6 sessions. A visual biofeedback helped the subjects in maintaining a symmetrical contribution of the two legs during pedaling. Participants were assessed before, after training and at follow-up visits (one week after treatment). Outcome measures were the unbalance during a pedaling test, and the temporal, spatial, and symmetry parameters during gait analysis.</p> <p>Results and discussion</p> <p>Three clusters, mainly differing in terms of gait speed, were identified and participants, representative of each cluster, were selected.</p> <p>An intra-subject statistical analysis (ANOVA) showed that all patients significantly decreased the pedaling unbalance after treatment and maintained significant improvements with respect to baseline at follow-up. The 2-week treatment induced some modifications in the gait pattern of two patients: one, the most impaired, significantly improved mean velocity and increased gait symmetry; the other one reduced significantly the over-compensation of the healthy limb. No benefits were produced in the gait of the last subject who maintained her slow but almost symmetrical pattern. Thus, this study might suggest that the treatment can be beneficial for patients having a very asymmetrical and inefficient gait and for those that overuse the healthy leg.</p> <p>Conclusion</p> <p>The results demonstrated that the treatment is feasible and it might be effective in translating progresses from pedaling to locomotion. If these results are confirmed on a larger and controlled scale, the intervention, thanks to its safety and low price, could have a significant impact as a home- rehabilitation treatment for chronic stroke patients.</p

Maternal caregiving moderates the impact of antenatal maternal cortisol on infant stress regulation

Background Emerging evidence suggests that antenatal exposure to maternal stress signals affects the development of the infant stress response systems. Animal studies indicate that maternal sensitive caregiving can reverse some of these effects. However, the generalizability of these findings to humans is unknown. This study investigated the role of maternal caregiving in the association between multiple markers of maternal antenatal stress and infant stress regulation. Methods The sample consisted of 94 mother-infant (N = 47 males, mean postnatal weeks = 12; SD = 1.84) dyads. Maternal levels of Interleukin-6, C-Reactive Protein (CRP), diurnal cortisol and alpha amylase, depressive and anxiety symptoms were assessed in late pregnancy (mean gestational age = 34.76; SD = 1.12), whereas postnatal symptomatology, caregiving, and infant cortisol response to the inoculation were evaluated at 3 months. Results Hierarchical linear models (HLMs) showed a significant interaction between maternal antenatal cortisol, caregiving, and time on infant cortisol reactivity, while controlling for gender, maternal age, and postnatal depression. Specifically, higher levels of maternal antenatal cortisol were associated with greater cortisol response only among infants of less emotionally available mothers. All other markers of antenatal stress were not significantly associated with infant cortisol reactivity either independently or in interaction with maternal caregiving. Conclusions Albeit preliminary, results provide the first evidence in humans that maternal sensitive caregiving may eliminate the association between antenatal maternal cortisol and infant cortisol regulation

Artificial neural network EMG classifier for functional hand grasp movements prediction

To design and implement an electromyography (EMG)-based controller for a hand robotic assistive device, which is able to classify the user's motion intention before the effective kinematic movement execution

Simultaneous measurements of kinematics and fMRI: compatibility assessment and case report on recovery evaluation of one stroke patient

<p>Abstract</p> <p>Background</p> <p>Correlating the features of the actual executed movement with the associated cortical activations can enhance the reliability of the functional Magnetic Resonance Imaging (fMRI) data interpretation. This is crucial for longitudinal evaluation of motor recovery in neurological patients and for investigating detailed mutual interactions between activation maps and movement parameters.</p> <p>Therefore, we have explored a new set-up combining fMRI with an optoelectronic motion capture system, which provides a multi-parameter quantification of the performed motor task.</p> <p>Methods</p> <p>The cameras of the motion system were mounted inside the MR room and passive markers were placed on the subject skin, without any risk or encumbrance. The versatile set-up allows 3-dimensional multi-segment acquisitions including recording of possible mirror movements, and it guarantees a high inter-sessions repeatability.</p> <p>We demonstrated the integrated set-up reliability through compatibility tests. Then, an fMRI block-design protocol combined with kinematic recordings was tested on a healthy volunteer performing finger tapping and ankle dorsal- plantar-flexion. A preliminary assessment of clinical applicability and perspectives was carried out by pre- and post rehabilitation acquisitions on a hemiparetic patient performing ankle dorsal- plantar-flexion. For all sessions, the proposed method integrating kinematic data into the model design was compared with the standard analysis.</p> <p>Results</p> <p>Phantom acquisitions demonstrated the not-compromised image quality. Healthy subject sessions showed the protocols feasibility and the model reliability with the kinematic regressor. The patient results showed that brain activation maps were more consistent when the images analysis included in the regression model, besides the stimuli, the kinematic regressor quantifying the actual executed movement (movement timing and amplitude), proving a significant model improvement. Moreover, concerning motor recovery evaluation, after one rehabilitation month, a greater cortical area was activated during exercise, in contrast to the usual focalization associated with functional recovery. Indeed, the availability of kinematics data allows to correlate this wider area with a higher frequency and a larger amplitude of movement.</p> <p>Conclusions</p> <p>The kinematic acquisitions resulted to be reliable and versatile to enrich the fMRI images information and therefore the evaluation of motor recovery in neurological patients where large differences between required and performed motion can be expected.</p

Neuroendocrine and immune markers of maternal stress during pregnancy and infant cognitive development

Antenatal exposure to maternal stress is a factor that may impact on offspring cognitive development. While some evidence exists of an association between maternal antenatal depressive or anxiety symptoms and infants’ cognitive outcomes, less is known about the role of biological indices of maternal antenatal stress in relation to infant cognitive development. The current study investigated the association between maternal depressive and anxiety symptoms, stress and inflammatory markers during pregnancy and infant’s cognitive development in a sample of 104 healthy pregnant women (mean gestational age=34.76; SD=1.12) and their 12-week-old infants (mean postnatal weeks=11.96; SD=1.85). Maternal depressive and anxiety symptoms were evaluated during pregnancy, alongside measurements of serum Interleukin-6 (IL-6), C-Reactive Protein (CRP), salivary cortisol and alpha amylase (sAA) concentrations. Infant cognitive development, maternal caregiving and concurrent anxiety or depressive symptoms were assessed 12 weeks after delivery. Hierarchical linear regressions indicated that higher maternal diurnal cortisol and CRP levels were independently associated with lower infant cognitive development scores, while adjusting for infant gender and gestational age, maternal IQ, caregiving, depressive or anxiety symptoms. Though correlational, findings seem suggestive of a role for variation in maternal biological stress signals during pregnancy in influencing infants’ early cognitive development

Latent classes of emotional and behavioural problems in epidemiological and referred samples and their relations to DSM-IV diagnoses

Researchers\u2019 interest have recently moved toward the identification of recurrent psychopathological profiles characterized by concurrent elevations on different behavioural and emotional traits. This new strategy turned to be useful in terms of diagnosis and outcome prediction. We used a person-centred statistical approach to examine whether different groups could be identified in a referred sample and in a general-population sample of children and adolescents, and we investigated their relation to DSM-IV diagnoses. A latent class analysis (LCA) was performed on the Child Behaviour Checklist (CBCL) syndrome scales of the referred sample (N = 1225), of the general-population sample (N = 3418), and of the total sample. Models estimating 1-class through 5-class solutions were compared and agreement in the classification of subjects was evaluated. Chi square analyses, a logistic regression, and a multinomial logistic regression analysis were used to investigate the relations between classes and diagnoses. In the two samples and in the total sample, the best-fitting models were 4-class solutions. The identified classes were Internalizing Problems (15.68%), Severe Dysregulated (7.82%), Attention/Hyperactivity (10.19%), and Low Problems (66.32%). Subsequent analyses indicated a significant relationship between diagnoses and classes as well as a main association between the severe dysregulated class and comorbidity. Our data suggested the presence of four different psychopathological profiles related to different outcomes in terms of psychopathological diagnoses. In particular, our results underline the presence of a profile characterized by severe emotional and behavioural dysregulation that is mostly associated with the presence of multiple diagnosis

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