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- Bridel, Claire, et al.
(författare)
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Diagnostic Value of Cerebrospinal Fluid Neurofilament Light Protein in Neurology : A Systematic Review and Meta-analysis
- 2019
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Ingår i: JAMA Neurology. - : American Medical Association (AMA). - 2168-6149 .- 2168-6157. ; 76:9, s. 1035-1048
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Forskningsöversikt (refereegranskat)abstract
- Importance Neurofilament light protein (NfL) is elevated in cerebrospinal fluid (CSF) of a number of neurological conditions compared with healthy controls (HC) and is a candidate biomarker for neuroaxonal damage. The influence of age and sex is largely unknown, and levels across neurological disorders have not been compared systematically to date.Objectives To assess the associations of age, sex, and diagnosis with NfL in CSF (cNfL) and to evaluate its potential in discriminating clinically similar conditions.Data Sources PubMed was searched for studies published between January 1, 2006, and January 1, 2016, reporting cNfL levels (using the search terms neurofilament light and cerebrospinal fluid) in neurological or psychiatric conditions and/or in HC.Study Selection Studies reporting NfL levels measured in lumbar CSF using a commercially available immunoassay, as well as age and sex.Data Extraction and Synthesis Individual-level data were requested from study authors. Generalized linear mixed-effects models were used to estimate the fixed effects of age, sex, and diagnosis on log-transformed NfL levels, with cohort of origin modeled as a random intercept.Main Outcome and Measure The cNfL levels adjusted for age and sex across diagnoses.Results Data were collected for 10 059 individuals (mean [SD] age, 59.7 [18.8] years; 54.1% female). Thirty-five diagnoses were identified, including inflammatory diseases of the central nervous system (n = 2795), dementias and predementia stages (n = 4284), parkinsonian disorders (n = 984), and HC (n = 1332). The cNfL was elevated compared with HC in a majority of neurological conditions studied. Highest levels were observed in cognitively impaired HIV-positive individuals (iHIV), amyotrophic lateral sclerosis, frontotemporal dementia (FTD), and Huntington disease. In 33.3% of diagnoses, including HC, multiple sclerosis, Alzheimer disease (AD), and Parkinson disease (PD), cNfL was higher in men than women. The cNfL increased with age in HC and a majority of neurological conditions, although the association was strongest in HC. The cNfL overlapped in most clinically similar diagnoses except for FTD and iHIV, which segregated from other dementias, and PD, which segregated from atypical parkinsonian syndromes.Conclusions and Relevance These data support the use of cNfL as a biomarker of neuroaxonal damage and indicate that age-specific and sex-specific (and in some cases disease-specific) reference values may be needed. The cNfL has potential to assist the differentiation of FTD from AD and PD from atypical parkinsonian syndromes.
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| 2. |
- Östh, Jonas, 1983, et al.
(författare)
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Muscle Activation Strategies in Human Body Models for the Development of Integrated Safety
- 2015
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Ingår i: The 24th ESV Conference Proceedings.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Human Body Models (HBMs) have been used in crash safety research for some time, and are now emerging as tools for the development of restraints systems. One important challenge in the development of advanced restraint systems is to integrate sensory information about the pre-crash phase (time to collision, impact speed and direction, occupant position) to alter restraint activation parameters. Restraint activation can begin even before the beginning of an impact, providing additional time to reposition or restrain the occupant. However, any such pre-crash intervention would invoke a muscle response that needs to be taken into account in HBMs used in simulation of integrated restraints. The objective of this paper is to provide an update on state-of-the-art modeling techniques for active musculature in HBMs. Examples of applications are presented, to illustrate future challenges in modeling of car occupants muscle responses to restraint activation.The most common approach for modeling active muscle force in HBMs is to use Hill-type models, in which the force produced is a function of muscle length, shortening velocity, and activation level. Active musculature was first implemented in cervical spine models. These models were applied to study occupant kinematic responses and injury outcome in rear-end, lateral, and frontal impacts; it was found that active musculature is essential for studying the response of the cervical spine. One approach utilized to represent muscle activity in HBMs is to use experimentally recorded muscle activities or activity levels acquired through inverse optimization in open-loop. More recently, in order to represent car occupant muscle responses in pre-crash situations, closed-loop control has been implemented for multibody and finite element HBMs, allowing the models to maintain their posture and simulate reflexive responses. Studies with these models showed that in addition to feedback control, anticipatory postural responses needs to be included to represent driver actions such as voluntary braking. Current HBMs have the capacity to model (utilizing closed-loop control) active muscle responses of car occupants in longitudinal pre-crash events. However, models have only been validated for limited sets of data since as high quality volunteer data, although it exists, is scarce. Omni-directional muscle responses have been implemented to some extent, but biofidelity of the simulated muscle activation schemes has not been assessed. Additional experimental volunteer muscle activity measurements (with normalized electromyogram recordings) in complex 3D-loading scenarios are needed for validation and to investigate how muscle recruitment depends on occupant awareness and varies between individuals. Further model development and validation of muscle activations schemes are necessary, for instance startle responses, and individual muscle control. This could improve assessment of restraint performance in complex accident scenarios, such as multiple impacts, far-side impacts and roll-over situations.
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