Articles: biological-models.
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Traumatic brain injury (TBI) is a major public health challenge that is also the third leading cause of death worldwide. It is also the leading cause of long-term disability in children and young adults worldwide. Despite a large body of research using predominantly in vivo and in vitro rodent models of brain injury, there is no medication that can reduce brain damage or promote brain repair mainly due to our lack of understanding in the mechanisms and pathophysiology of the TBI. ⋯ The in vitro studies reviewed here examined key processes in TBI pathophysiology such as membrane disruptions leading to ionic dysregulation, inflammation, and the subsequent damages to the microtubules and axons, as well as cell death. Overall, the studies examined in this review contributed to the betterment of our understanding of TBI as a disease process. Yet, our review also revealed the areas where more work needs to be done such as: 1) diversification of load application methods that will include complex loading that mimics in vivo head impacts; 2) more widespread use of human brain cells, especially patient-matched human cells in the experimental set-up; and 3) need for building a more high-throughput system to be able to discover effective therapeutic targets for TBI.
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Lancet Digit Health · Sep 2021
Observational StudyEarly detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study.
Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection foci. To date, these models have only considered the culmination or peak of symptoms, which is not suitable for the early detection of infection. We aimed to estimate the probability of an individual being infected with SARS-CoV-2 on the basis of early self-reported symptoms to enable timely self-isolation and urgent testing. ⋯ ZOE, the UK Government Department of Health and Social Care, the Wellcome Trust, the UK Engineering and Physical Sciences Research Council, the UK National Institute for Health Research, the UK Medical Research Council, the British Heart Foundation, the Alzheimer's Society, the Chronic Disease Research Foundation, and the Massachusetts Consortium on Pathogen Readiness.
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Journal of neurotrauma · Aug 2021
Numerical models of spinal cord trauma: The effect of cerebrospinal fluid pressure and epidural fat on the results.
Spinal cord injury (SCI) is commonly caused by traumatic mechanical damage. Although numerical models can help predict the mechanics of SCI without putting the subjects in danger, previous studies did not focus on alternations in cerebrospinal fluid (CSF) pressure and did not account for the presence of epidural fat. This study aims to numerically compare the mechanical behavior of the human spine when subjected to contusion and burst fracture with varying CSF pressure, either normal or elevated pressure that represents intracranial hypertension. ⋯ The comparison of the CSF pressures demonstrated that SCI in patients with elevated pressure and in regions where insufficient epidural fat exists might lead to higher spinal cord stresses. Yet, in regions with enough fat, the fat can absorb energy and counteract the effect of the elevated pressure. These results indicate important aspects that need to be accounted for in future numerical models of SCI while also demonstrating how the injury might be aggravated by preexisting conditions.
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The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy. ⋯ This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.