Neonatal head and torso vibration exposure during inter-hospital transfer

Inter-hospital transport of premature infants is increasingly common, given the centralisation of neonatal intensive care. However, it is known to be associated with anomalously increased morbidity, most notably brain injury, and with increased mortality from multifactorial causes. Surprisingly, there have been relatively few previous studies investigating the levels of mechanical shock and vibration hazard present during this vehicular transport pathway. Using a custom inertial datalogger, and analysis software, we quantify vibration and linear head acceleration. Mounting multiple inertial sensing units on the forehead and torso of neonatal patients and a preterm manikin, and on the chassis of transport incubators over the duration of inter-site transfers, we find that the resonant frequency of the mattress and harness system currently used to secure neonates inside incubators is ~9Hz. This couples to vehicle chassis vibration, increasing vibration exposure to the neonate. The vibration exposure per journey (A(8) using the ISO 2631 standard) was at least 20% of the action point value of current European Union regulations over all 12 neonatal transports studied, reaching 70% in two cases. Direct injury risk from linear head acceleration (HIC15) was negligible. Although the overall hazard was similar, vibration isolation differed substantially between sponge and air mattresses, with a manikin. Using a Global Positioning System datalogger alongside inertial sensors, vibration increased with vehicle speed only above 60 km/h. These preliminary findings suggest there is scope to engineer better systems for transferring sick infants, thus potentially improving their outcomes

A knowledge map analysis of brain biomechanics: Current evidence and future directions

Although brain, one of the most complex organs in the mammalian body, has been subjected to many studies from physiological and pathological points of view, there remain significant gaps in the available knowledge regarding its biomechanics. This article reviews the research trends in brain biomechanics with a focus on injury. We used published scientific articles indexed by Web of Science database over the past 40 years and tried to address the gaps that still exist in this field. We analyzed the data using VOSviewer, which is a software tool designed for scientometric studies. The results of this study showed that the response of brain tissue to external forces has been one of the significant research topics among biomechanicians. These studies have addressed the effects of mechanical forces on the brain and mechanisms of traumatic brain injury, as well as characterized changes in tissue behavior under trauma and other neurological diseases to provide new diagnostic and monitoring methods. In this study, some challenges in the field of brain injury biomechanics have been identified and new directions toward understanding the gaps in this field are suggested.12 month embargo; published online: 1 May 2020This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Tension Strain-Softening and Compression Strain-Stiffening Behavior of Brain White Matter

Brain, the most important component of the central nervous system (CNS), is a soft tissue with a complex structure. Understanding the role of brain tissue microstructure in mechanical properties is essential to have a more profound knowledge of how brain development, disease, and injury occur. While many studies have investigated the mechanical behavior of brain tissue under various loading conditions, there has not been a clear explanation for variation reported for material properties of brain tissue. The current study compares the ex-vivo mechanical properties of brain tissue under two loading modes, namely compression and tension, and aims to explain the differences observed by closely examining the microstructure under loading. We tested bovine brain samples under uniaxial tension and compression loading conditions, and fitted hyperelastic material parameters. At 20% strain, we observed that the shear modulus of brain tissue in compression is about 6 times higher than in tension. In addition, we observed that brain tissue exhibited strain-stiffening in compression and strain-softening in tension. In order to investigate the effect of loading modes on the tissue microstructure, we fixed the samples using a novel method that enabled keeping the samples at the loaded stage during the fixation process. Based on the results of histology, we hypothesize that during compressive loading, the strain-stiffening behavior of the tissue could be attributed to glial cell bodies being pushed against surroundings, contacting each other and resisting compression, while during tension, cell connections are detached and the tissue displays softening behavior.12 month embargo; published online: 3 June 2020This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

End to end stroke triage using cerebrovascular morphology and machine learning

Background: Rapid and accurate triage of acute ischemic stroke (AIS) is essential for early revascularization and improved patient outcomes. Response to acute reperfusion therapies varies significantly based on patient-specific cerebrovascular anatomy that governs cerebral blood flow. We present an end-to-end machine learning approach for automatic stroke triage. Methods: Employing a validated convolutional neural network (CNN) segmentation model for image processing, we extract each patient’s cerebrovasculature and its morphological features from baseline non-invasive angiography scans. These features are used to detect occlusion’s presence and the site automatically, and for the first time, to estimate collateral circulation without manual intervention. We then use the extracted cerebrovascular features along with commonly used clinical and imaging parameters to predict the 90 days functional outcome for each patient. Results: The CNN model achieved a segmentation accuracy of 94% based on the Dice similarity coefficient (DSC). The automatic stroke detection algorithm had a sensitivity and specificity of 92% and 94%, respectively. The models for occlusion site detection and automatic collateral grading reached 96% and 87.2% accuracy, respectively. Incorporating the automatically extracted cerebrovascular features significantly improved the 90 days outcome prediction accuracy from 0.63 to 0.83. Conclusion: The fast, automatic, and comprehensive model presented here can improve stroke diagnosis, aid collateral assessment, and enhance prognostication for treatment decisions, using cerebrovascular morphology. Copyright © 2023 Deshpande, Elliott, Jiang, Tahsili-Fahadan, Kidwell, Wintermark and Laksari.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Automatic segmentation, feature extraction and comparison of healthy and stroke cerebral vasculature.

Accurate segmentation of cerebral vasculature and a quantitative assessment of its morphology is critical to various diagnostic and therapeutic purposes and is pertinent to studying brain health and disease. However, this is still a challenging task due to the complexity of the vascular imaging data. We propose an automated method for cerebral vascular segmentation without the need of any manual intervention as well as a method to skeletonize the binary segmented map to extract vascular geometric features and characterize vessel structure. We combine a Hessian-based probabilistic vessel-enhancing filtering with an active-contour-based technique to segment magnetic resonance and computed tomography angiograms (MRA and CTA) and subsequently extract the vessel centerlines and diameters to calculate the geometrical properties of the vasculature. Our method was validated using a 3D phantom of the Circle-of-Willis region, demonstrating 84% mean Dice similarity coefficient (DSC) and 85% mean Pearson's correlation coefficient (PCC) with minimal modified Hausdorff distance (MHD) error (3 surface pixels at most), and showed superior performance compared to existing segmentation algorithms upon quantitative comparison using DSC, PCC and MHD. We subsequently applied our algorithm to a dataset of 40 subjects, including 1) MRA scans of healthy subjects (n = 10, age = 30 ± 9), 2) MRA scans of stroke patients (n = 10, age = 51 ± 15), 3) CTA scans of healthy subjects (n = 10, age = 62 ± 12), and 4) CTA scans of stroke patients (n = 10, age = 68 ± 11), and obtained a quantitative comparison between the stroke and normal vasculature for both imaging modalities. The vascular network in stroke patients compared to age-adjusted healthy subjects was found to have a significantly (p < 0.05) higher tortuosity (3.24 ± 0.88 rad/cm vs. 7.17 ± 1.61 rad/cm for MRA, and 4.36 ± 1.32 rad/cm vs. 7.80 ± 0.92 rad/cm for CTA), higher fractal dimension (1.36 ± 0.28 vs. 1.71 ± 0.14 for MRA, and 1.56 ± 0.05 vs. 1.69 ± 0.20 for CTA), lower total length (3.46 ± 0.99 m vs. 2.20 ± 0.67 m for CTA), lower total volume (61.80 ± 18.79 ml vs. 34.43 ± 22.9 ml for CTA), lower average diameter (2.4 ± 0.21 mm vs. 2.18 ± 0.07 mm for CTA), and lower average branch length (4.81 ± 1.97 mm vs. 8.68 ± 2.03 mm for MRA), respectively. We additionally studied the change in vascular features with respect to aging and imaging modality. While we observed differences between features as a result of aging, statistical analysis did not show any significant differences, whereas we found that the number of branches were significantly different (p < 0.05) between the two imaging modalities (201 ± 73 for MRA vs. 189 ± 69 for CTA). Our segmentation and feature extraction algorithm can be applied on any imaging modality and can be used in the future to automatically obtain the 3D segmented vasculature for diagnosis and treatment planning as well as to study morphological changes due to stroke and other cerebrovascular diseases (CVD) in the clinic

Palmitoylethanolamide as adjunctive therapy for autism: Efficacy and safety results from a randomized controlled trial

Inflammation as well as glutamate excitotoxicity have been proposed to participate in the propagation of autism. Palmitoylethanolamide (PEA) is an endocannabinoid proven to prevent glutamatergic toxicity and inhibit inflammatory responses simultaneously. The present randomized, parallel group, double-blind placebo-controlled trial is the first study depicted to probe the efficacy of co-treatment with risperidone and PEA over 10 weeks in children with autism. Seventy children (aged 4�12 years) with autism and moderate to severe symptoms of irritability were randomly assigned to two treatment regimens. The study outcomes were measured using the Aberrant Behavior Checklist-Community Edition (ABC-C). At trial endpoint (week 10), combination of PEA and risperidone had superior efficacy in ameliorating the ABC-irritability and hyperactivity/noncompliance symptoms (Cohen's d, 95 confidence interval (CI) = 0.94, 0.41 to 1.46, p = 0.001) compared with a risperidone plus placebo regimen. Interestingly, effect of combination treatment on hyperactivity symptoms was also observed at trial midpoint (week 5) but with a smaller effect size (d = 0.53, p = 0.04) than that at the endpoint (d = 0.94, p = 0.001). Meanwhile, there was a trend toward significance for superior effect of risperidone plus PEA over risperidone plus placebo on inappropriate speech at trial endpoint (d = 0.51, p = 0.051). No significant differences existed between the two treatment groups for the other two ABC-C subscales (lethargy/social withdrawal and stereotypic behavior). The findings suggest that PEA may augment therapeutic effects of risperidone on autism-related irritability and hyperactivity. Future studies are warranted to investigate whether PEA can serve as a stand-alone treatment for autism. © 2018 Elsevier Lt

Viscoelasticity of children and adolescent brains through MR elastography

Magnetic Resonance Elastography (MRE) is an elasticity imaging technique that allows a safe, fast, and non-invasive evaluation of the mechanical properties of biological tissues in vivo. Since mechanical properties reflect a tissue's composition and arrangement, MRE is a powerful tool for the investigation of the microstructural changes that take place in the brain during childhood and adolescence. The goal of this study was to evaluate the viscoelastic properties of the brain in a population of healthy children and adolescents in order to identify potential age and sex dependencies. We hypothesize that because of myelination, age dependent changes in the mechanical properties of the brain will occur during childhood and adolescence. Our sample consisted of 26 healthy individuals (13 M, 13 F) with age that ranged from 7-17 years (mean: 11.9 years). We performed multifrequency MRE at 40, 60, and 80 Hz actuation frequencies to acquire the complex-valued shear modulus G = G′ + iG″ with the fundamental MRE parameters being the storage modulus (G′), the loss modulus (G″), and the magnitude of complex-valued shear modulus (|G|). We fitted a springpot model to these frequency-dependent MRE parameters in order to obtain the parameter α, which is related to tissue's microstructure, and the elasticity parameter k, which was converted to a shear modulus parameter (μ) through viscosity (η). We observed no statistically significant variation in the parameter μ, but a significant increase of the microstructural parameter α of the white matter with increasing age (p < 0.05). Therefore, our MRE results suggest that subtle microstructural changes such as neural tissue's enhanced alignment and geometrical reorganization during childhood and adolescence could result in significant biomechanical changes. In line with previously reported MRE data for adults, we also report significantly higher shear modulus (μ) for female brains when compared to males (p < 0.05). The data presented here can serve as a clinical baseline in the analysis of the pediatric and adolescent brain's viscoelasticity over this age span, as well as extending our understanding of the biomechanics of brain development.24 month embargo; available online 19 December 2020This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]