Publications
6 results found
Atchison C, Davies B, Cooper E, et al., 2023, Long-term impact of COVID-19 among 242,712 adults in England, Nature Communications, Vol: 14, ISSN: 2041-1723
The COVID-19 pandemic is having a lasting impact on health and well-being. We compare current self-reported health, quality of life and symptom profiles for people with ongoing symptoms following COVID-19 to those who have never tested positive for SARS-CoV-2 infection and those who have recovered from COVID-19. Overall, 276,840/800,000 (34·6%) of invited participants took part. Mental health and health-related quality of life were worse among participants with ongoing persistent symptoms post-COVID compared with those who had never had COVID-19 or had recovered. In this study, median duration of COVID-related symptoms (N = 130,251) was 1·3 weeks (inter-quartile range 6 days to 2 weeks), with 7·5% and 5·2% reporting ongoing symptoms ≥12 weeks and ≥52 weeks respectively. Female sex, ≥1 comorbidity and being infected when Wild-type variant was dominant were associated with higher probability of symptoms lasting ≥12 weeks and longer recovery time in those with persistent symptoms. Although COVID-19 is usually of short duration, some adults experience persistent and burdensome illness.
Azor AM, Sharp DJ, Jolly AE, et al., 2022, Automation and standardization of subject-specific region-of-interest segmentation for investigation of diffusion imaging in clinical populations, PLOS ONE, Vol: 17, ISSN: 1932-6203
Duckworth H, Azor A, Wischmann N, et al., 2022, A finite element model of cerebral vascular injury for predicting microbleeds location, Frontiers in Bioengineering and Biotechnology, Vol: 10, ISSN: 2296-4185
Finite Element (FE) models of brain mechanics have improved our understanding of the brain response to rapid mechanical loads that produce traumatic brain injuries. However, these models have rarely incorporated vasculature, which limits their ability to predict the response of vessels to head impacts. To address this shortcoming, here we used high-resolution MRI scans to map the venous system anatomy at a submillimetre resolution. We then used this map to develop an FE model of veins and incorporated it in an anatomically detailed FE model of the brain. The model prediction of brain displacement at different locations was compared to controlled experiments on post-mortem human subject heads, yielding over 3,100 displacement curve comparisons, which showed fair to excellent correlation between them. We then used the model to predict the distribution of axial strains and strain rates in the veins of a rugby player who had small blood deposits in his white matter, known as microbleeds, after sustaining a head collision. We hypothesised that the distribution of axial strain and strain rate in veins can predict the pattern of microbleeds. We reconstructed the head collision using video footage and multi-body dynamics modelling and used the predicted head accelerations to load the FE model of vascular injury. The model predicted large axial strains in veins where microbleeds were detected. A region of interest analysis using white matter tracts showed that the tract group with microbleeds had 95th percentile peak axial strain and strain rate of 0.197 and 64.9 s−1 respectively, which were significantly larger than those of the group of tracts without microbleeds (0.163 and 57.0 s−1). This study does not derive a threshold for the onset of microbleeds as it investigated a single case, but it provides evidence for a link between strain and strain rate applied to veins during head impacts and structural damage and allows for future work to generate threshold valu
Jolly AE, Balaet M, Azor A, et al., 2021, Detecting axonal injury in individual patients after traumatic brain injury., Brain: a journal of neurology, Vol: 144, Pages: 92-113, ISSN: 0006-8950
Poor outcomes after traumatic brain injury (TBI) are common yet remain difficult to predict. Diffuse axonal injury is important for outcomes, but its assessment remains limited in the clinical setting. Currently, axonal injury is diagnosed based on clinical presentation, visible damage to the white matter or via surrogate markers of axonal injury such as microbleeds. These do not accurately quantify axonal injury leading to misdiagnosis in a proportion of patients. Diffusion tensor imaging provides a quantitative measure of axonal injury in vivo, with fractional anisotropy often used as a proxy for white matter damage. Diffusion imaging has been widely used in TBI but is not routinely applied clinically. This is in part because robust analysis methods to diagnose axonal injury at the individual level have not yet been developed. Here, we present a pipeline for diffusion imaging analysis designed to accurately assess the presence of axonal injury in large white matter tracts in individuals. Average fractional anisotropy is calculated from tracts selected on the basis of high test-retest reliability, good anatomical coverage and their association to cognitive and clinical impairments after TBI. We test our pipeline for common methodological issues such as the impact of varying control sample sizes, focal lesions and age-related changes to demonstrate high specificity, sensitivity and test-retest reliability. We assess 92 patients with moderate-severe TBI in the chronic phase (≥6 months post-injury), 25 patients in the subacute phase (10 days to 6 weeks post-injury) with 6-month follow-up and a large control cohort (n = 103). Evidence of axonal injury is identified in 52% of chronic and 28% of subacute patients. Those classified with axonal injury had significantly poorer cognitive and functional outcomes than those without, a difference not seen for focal lesions or microbleeds. Almost a third of patients with unremarkable standard MRIs had evidence o
Yu X, Azor A, Sharp DJ, et al., 2020, Mechanisms of tensile failure of cerebrospinal fluid in blast traumatic brain injury, Extreme Mechanics Letters, Vol: 38, Pages: 1-9, ISSN: 2352-4316
Mechanisms of blast-induced Traumatic Brain Injury (BTBI), particularly those linked to the primary pressure wave, are still not fully understood. One possible BTBI mechanism is cavitation in the cerebrospinal fluid (CSF) caused by CSF tensile failure, which is likely to increase strain and strain rate in the brain tissue near the CSF. Blast loading of the head can generate rarefaction (expansion) waves and rapid head motion, which both can produce tensile forces in the CSF. However, it is not clear which of these mechanisms is more likely to cause CSF tensile failure. In this study, we used a high-fidelity 3-dimensional computational model of the human head to test whether the CSF tensile failure increases brain deformation near the brain/CSF boundary and to determine the key failure mechanisms. We exposed the head model to a frontal blast wave and predicted strain and strain rate distribution in the cortex. We found that CSF tensile failure significantly increased strain and strain rate in the cortex. We then studied whether the rapid head motion or the rarefaction wave causes strain and strain rate concentration in cortex. We isolated these two effects by conducting simulations with pure head motion loading (i.e. prescribing the skull velocity but eliminating the pressure wave) and pure blast wave loading (i.e. eliminating head motion by fixing the skull base). Our results showed that the strain increase in the cortex was mainly caused by head motion. In contrast, strain rate increase was caused by both rapid head motion and rarefaction waves, but head motion had a stronger effect on elevating strain rate. Our results show that rapid motion of the head produced by blast wave is the key mechanism for CSF tensile failure and subsequent concentration of strain and strain rate in cortex. This finding suggests that mitigation of rapid head motion caused by blast loading needs to be addressed in the design of protective equipment in order to prevent the tensile failure
Azor AM, Cole JH, Holland AJ, et al., 2019, Increased brain age in adults with Prader-Willi syndrome, NeuroImage: Clinical, Vol: 21, ISSN: 2213-1582
Prader-Willi syndrome (PWS) is the most common genetic obesity syndrome, with associated learning difficulties, neuroendocrine deficits, and behavioural and psychiatric problems. As the life expectancy of individuals with PWS increases, there is concern that alterations in brain structure associated with the syndrome, as a direct result of absent expression of PWS genes, and its metabolic complications and hormonal deficits, might cause early onset of physiological and brain aging. In this study, a machine learning approach was used to predict brain age based on grey matter (GM) and white matter (WM) maps derived from structural neuroimaging data using T1-weighted magnetic resonance imaging (MRI) scans. Brain-predicted age difference (brain-PAD) scores, calculated as the difference between chronological age and brain-predicted age, are designed to reflect deviations from healthy brain aging, with higher brain-PAD scores indicating premature aging. Two separate adult cohorts underwent brain-predicted age calculation. The main cohort consisted of adults with PWS (n = 20; age mean 23.1 years, range 19.8-27.7; 70.0% male; body mass index (BMI) mean 30.1 kg/m2, 21.5-47.7; n = 19 paternal chromosome 15q11-13 deletion) and age- and sex-matched controls (n = 40; age 22.9 years, 19.6-29.0; 65.0% male; BMI 24.1 kg/m2, 19.2-34.2) adults (BMI PWS vs. control P = .002). Brain-PAD was significantly greater in PWS than controls (effect size mean ± SEM +7.24 ± 2.20 years [95% CI 2.83, 11.63], P = .002). Brain-PAD remained significantly greater in PWS than controls when restricting analysis to a sub-cohort matched for BMI consisting of n = 15 with PWS with BMI range 21.5-33.7 kg/m2, and n = 29 controls with BMI 21.7-34.2 kg/m2 (effect size +5.51 ± 2.56 years [95% CI 3.44, 10.38], P = .037). In the PWS group, brain-PAD scores were not associated with intelligence quotient (IQ), use of hormonal and psychotropic medications, nor severity of repetitive or disruptive
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