1. |
- Needham, Edward J, et al.
(författare)
-
Complex Autoantibody Responses Occur following Moderate to Severe Traumatic Brain Injury.
- 2021
-
Ingår i: Journal of immunology. - : The American Association of Immunologists. - 1550-6606 .- 0022-1767. ; 207:1, s. 90-100
-
Tidskriftsartikel (refereegranskat)abstract
- Most of the variation in outcome following severe traumatic brain injury (TBI) remains unexplained by currently recognized prognostic factors. Neuroinflammation may account for some of this difference. We hypothesized that TBI generated variable autoantibody responses between individuals that would contribute to outcome. We developed a custom protein microarray to detect autoantibodies to both CNS and systemic Ags in serum from the acute-phase (the first 7 d), late (6-12 mo), and long-term (6-13 y) intervals after TBI in human patients. We identified two distinct patterns of immune response to TBI. The first was a broad response to the majority of Ags tested, predominantly IgM mediated in the acute phase, then IgG dominant at late and long-term time points. The second was responses to specific Ags, most frequently myelin-associated glycopeptide (MAG), which persisted for several months post-TBI but then subsequently resolved. Exploratory analyses suggested that patients with a greater acute IgM response experienced worse outcomes than predicted from current known risk factors, suggesting a direct or indirect role in worsening outcome. Furthermore, late persistence of anti-MAG IgM autoantibodies correlated with raised serum neurofilament light concentrations at these time points, suggesting an association with ongoing neurodegeneration over the first year postinjury. Our results show that autoantibody production occurs in some individuals following TBI, can persist for many years, and is associated with worse patient outcome. The complexity of responses means that conventional approaches based on measuring responses to single antigenic targets may be misleading.
|
|
2. |
- Kamnitsas, Konstantinos, et al.
(författare)
-
Transductive Image Segmentation : Self-training and Effect of Uncertainty Estimation
- 2021
-
Ingår i: Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health - 3rd MICCAI Workshop, DART 2021, and 1st MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Proceedings. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783030877217 ; 12968 LNCS, s. 79-89
-
Konferensbidrag (refereegranskat)abstract
- Semi-supervised learning (SSL) uses unlabeled data during training to learn better models. Previous studies on SSL for medical image segmentation focused mostly on improving model generalization to unseen data. In some applications, however, our primary interest is not generalization but to obtain optimal predictions on a specific unlabeled database that is fully available during model development. Examples include population studies for extracting imaging phenotypes. This work investigates an often overlooked aspect of SSL, transduction. It focuses on the quality of predictions made on the unlabeled data of interest when they are included for optimization during training, rather than improving generalization. We focus on the self-training framework and explore its potential for transduction. We analyze it through the lens of Information Gain and reveal that learning benefits from the use of calibrated or under-confident models. Our extensive experiments on a large MRI database for multi-class segmentation of traumatic brain lesions shows promising results when comparing transductive with inductive predictions. We believe this study will inspire further research on transductive learning, a well-suited paradigm for medical image analysis.
|
|
3. |
- Whitehouse, Daniel P., et al.
(författare)
-
Blood biomarkers and structural imaging correlations post-traumatic brain injury : A systematic review
- 2021
-
Ingår i: Neurosurgery. - : Wolters Kluwer. - 0148-396X .- 1524-4040. ; 90:2, s. 170-179
-
Forskningsöversikt (refereegranskat)abstract
- Background: Blood biomarkers are of increasing importance in the diagnosis and assessment of traumatic brain injury (TBI). However, the relationship between them and lesions seen on imaging remains unclear.Objective: To perform a systematic review of the relationship between blood biomarkers and intracranial lesion types, intracranial lesion injury patterns, volume/number of intracranial lesions, and imaging classification systems.Methods: We searched Medical Literature Analysis and Retrieval System Online, Excerpta Medica dataBASE, and Cumulative Index to Nursing and Allied Health Literature from inception to May 2021, and the references of included studies were also screened. Heterogeneity in study design, biomarker types, imaging modalities, and analyses inhibited quantitative analysis, with a qualitative synthesis presented.Results: Fifty-nine papers were included assessing one or more biomarker to imaging comparisons per paper: 30 assessed imaging classifications or injury patterns, 28 assessed lesion type, and 11 assessed lesion volume or number. Biomarker concentrations were associated with the burden of brain injury, as assessed by increasing intracranial lesion volume, increasing numbers of traumatic intracranial lesions, and positive correlations with imaging classification scores. There were inconsistent findings associating different biomarkers with specific imaging phenotypes including diffuse axonal injury, cerebral edema, and intracranial hemorrhage.Conclusion: Blood-based biomarker concentrations after TBI are consistently demonstrated to correlate burden of intracranial disease. The relation with specific injury types is unclear suggesting a lack of diagnostic specificity and/or is the result of the complex and heterogeneous nature of TBI.
|
|