Brain transforms natural killer cells that exacerbate brain edema after intracerebral hemorrhage.
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ABSTRACT:
SUBMITTER: Li Z
PROVIDER: S-EPMC7526480 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
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Brain transforms natural killer cells that exacerbate brain edema after intracerebral hemorrhage.
Li Zhiguo Z Li Minshu M Shi Samuel X SX Yao Nan N Cheng Xiaojing X Guo Ai A Zhu Zilong Z Zhang Xiaoan X Liu Qiang Q
The Journal of experimental medicine 20201201 12
Perihematomal edema (PHE) occurs within hours after intracerebral hemorrhage (ICH), leading to secondary injury manifested by impaired blood-brain barrier (BBB) integrity and destruction of adjacent tissue. To dissect the mechanisms underlying PHE formation, we profiled human and mouse perihematomal tissues and identified natural killer (NK) cells as the predominant immune cell subset that outnumbers other infiltrating immune cell types during early stages of ICH. Unbiased clustering of single-c ...[more]
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| S-EPMC7048624 | biostudies-literature
Project description:BackgroundLiver disease is associated with altered serum osmolality, increased thrombin generation, and systemic inflammation, all of which may contribute to perihematomal edema (PHE) after intracerebral hemorrhage (ICH). We evaluated the association between a validated liver fibrosis index and PHE growth in a cohort of patients with primary ICH.MethodsWe performed a retrospective cohort study using data from the Virtual International Stroke Trials Archive-ICH. We included adult patients with primary ICH presenting within 6 h of symptom onset. The exposure of interest was the Fibrosis-4 (FIB-4) score, a validated liver fibrosis index; this was modeled as a continuous variable. The primary outcome was absolute PHE growth over 96 h. Secondary outcomes were absolute admission and 96-h PHE volumes. We used multiple linear regression models adjusted for established determinants of PHE. In a secondary analysis, the FIB-4 score was modeled as a categorical variable to compare patients with versus without liver fibrosis.ResultsAmong 354 patients with ICH, 8% had evidence of liver fibrosis based on a validated cutoff. The FIB-4 score was not associated with PHE growth in unadjusted (β, 0.03; 95% CI, - 0.01 to 0.12) or adjusted models (β, 0.04; 95% CI, - 0.03 to 0.13). In a secondary analysis treating FIB-4 as a categorical variable, patients with liver fibrosis did not have greater PHE growth than those without liver fibrosis. FIB-4 score was also not associated with absolute admission or 96-h PHE volumes.ConclusionsIn a multicenter cohort of patients with primary intracerebral hemorrhage, a liver fibrosis score was not associated with PHE volume or growth.
| S-EPMC7887133 | biostudies-literature
Project description:Background and Purpose- Volumes of hemorrhage and perihematomal edema (PHE) are well-established biomarkers of primary and secondary injury, respectively, in spontaneous intracerebral hemorrhage. An automated imaging pipeline capable of accurately and rapidly quantifying these biomarkers would facilitate large cohort studies evaluating underlying mechanisms of injury. Methods- Regions of hemorrhage and PHE were manually delineated on computed tomography scans of patients enrolled in 2 intracerebral hemorrhage studies. Manual ground-truth masks from the first cohort were used to train a fully convolutional neural network to segment images into hemorrhage and PHE. The primary outcome was automated-versus-human concordance in hemorrhage and PHE volumes. The secondary outcome was voxel-by-voxel overlap of segmentations, quantified by the Dice similarity coefficient (DSC). Algorithm performance was validated on 84 scans from the second study. Results- Two hundred twenty-four scans from 124 patients with supratentorial intracerebral hemorrhage were used for algorithm derivation. Median volumes were 18 mL (interquartile range, 8-43) for hemorrhage and 12 mL (interquartile range, 5-30) for PHE. Concordance was excellent (0.96) for automated quantification of hemorrhage and good (0.81) for PHE, with DSC of 0.90 (interquartile range, 0.85-0.93) and 0.54 (0.39-0.65), respectively. External validation confirmed algorithm accuracy for hemorrhage (concordance 0.98, DSC 0.90) and PHE (concordance 0.90, DSC 0.55). This was comparable with the consistency observed between 2 human raters (DSC 0.90 for hemorrhage, 0.57 for PHE). Conclusions- We have developed a deep learning-based imaging algorithm capable of accurately measuring hemorrhage and PHE volumes. Rapid and consistent automated biomarker quantification may accelerate powerful and precise studies of disease biology in large cohorts of intracerebral hemorrhage patients.
| S-EPMC6993878 | biostudies-literature